CoinDCX Report Says India Sees Rising Women Crypto Investors with 116.8% Surge

TheNewsCryptoОпубліковано о 2026-03-07Востаннє оновлено о 2026-03-07

Анотація

According to a CoinDCX report published on March 6, India has experienced a significant 116.8% yearly surge in women cryptocurrency investors, increasing their representation in the investor base to over 15%. The ratio of female to male investors improved from 1:7 to 1:6 between 2023 and 2025. The growth spans both metropolitan and non-metropolitan regions, with nearly 50% of women investors in Tier I cities and over 40% in Tier II and non-metro areas. Women typically hold an average of four tokens, including Bitcoin, Ethereum, Solana, Polygon, and XRP. The report highlights that women's growing participation is driven by a desire for financial ownership and independence, not just profits, with many starting with small investments and a focus on education. Cities like Mumbai, Delhi, and Kolkata lead in adoption, but emerging hubs such as Bhubaneswar, Vadodara, and Kochi are also seeing increased activity.

As more women enter the Indian cryptocurrency market, the overall crypto picture has evolved significantly. According to the most recent CoinDCX report published on March 6, the number of female investors rose by 116.8% in the most recent yearly growth cycle, which reflects increasing participation across both metro and non-metro regions, between 2023 and 2025, from 1:7 to 1:6.

The report revealed, “Women now account for over 15% of the total investor base, highlighting a steady shift toward greater financial participation in digital assets.” Also, the data clearly mentioned that the preference for Bitcoin and Ethereum, along with broad exposure to assets like Solana, Polygon, and XRP. Also, women owned an average of four tokens in their portfolio that include Bitcoin, Ethereum, Polygon, Solana, Cardano, XRP, Dogecoin, Shiba Inu, and Avalanche.

Expands Beyond Major Cities

Also, the report stated that participation is spreading geographically outside of established financial centers. While nearly 50% of female investors are found in Tier I cities, over 40% currently participate from Tier II and non-metropolitan areas, indicating greater financial inclusion through digital-first investing platforms.

While the report found that major cities like Mumbai, Delhi, and Kolkata have the highest levels of participation in India, and developing cities like Bhubaneswar, Vadodara, and Kochi are also witnessing an increase in the use of cryptocurrencies. These patterns are based on true accounts of women who prioritize confidence and financial freedom over hesitancy.

The report concludes that women from a variety of backgrounds and cities are embracing the cryptocurrency market because they want financial ownership rather than just possible profits. Many begin with modest investments and an emphasis on education, but the true change is psychological as they become active decision-makers rather than merely passive participants in financial conversations.

Пов'язані питання

QWhat is the percentage increase in female crypto investors according to the CoinDCX report?

AThe number of female crypto investors in India rose by 116.8% in the most recent yearly growth cycle.

QWhat is the total investor base do women now account for, as per the report?

AWomen now account for over 15% of the total investor base.

QWhich major cryptocurrencies do women investors in India prefer, as mentioned in the report?

AThe report mentions a preference for Bitcoin and Ethereum, along with broad exposure to assets like Solana, Polygon, and XRP.

QWhat geographical shift in participation does the CoinDCX report highlight?

AThe report states that participation is spreading beyond major cities, with over 40% of female investors currently participating from Tier II and non-metropolitan areas.

QWhat is the primary motivation for women entering the crypto market, according to the report's conclusion?

AThe report concludes that women are embracing cryptocurrency because they want financial ownership and to be active decision-makers, rather than just for possible profits.

Пов'язані матеріали

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist50 хв тому

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist50 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit50 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit50 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit55 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit55 хв тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 год тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit2 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit2 год тому

Торгівля

Спот