Coinbase Users Locked Out: Unable To Buy, Sell, Or Transfer Crypto

bitcoinistPublicado a 2026-02-12Actualizado a 2026-02-12

Resumen

Coinbase experienced a significant service disruption on Wednesday, preventing users from buying, selling, or transferring cryptocurrency. The outage occurred just before the company's Q4 2025 earnings report, causing its stock (COIN) to drop 8% to $140. Coinbase acknowledged the issue on social media, deployed a fix, and assured users that funds were secure, but did not disclose the root cause. Ahead of earnings, Monness Crespi double-downgraded COIN from buy to sell, citing a $120 price target and predicting continued weakness in the crypto market.

Cryptocurrency exchange Coinbase (COIN) experienced an unexpected service disruption on Wednesday, just hours before the company is scheduled to report its fourth‐quarter 2025 earnings.

The outage left users temporarily unable to buy, sell, or transfer digital assets on the platform, triggering concern among customers and adding pressure to the company’s stock.

Platform Disruption Hits Coinbase

In a post on X (previously Twitter) Coinbase acknowledged the issue, stating that some customers were unable to conduct transactions on the platform. The company assured users that it was investigating the problem and emphasized that customer funds remained secure.

Shortly afterward, Coinbase Support announced that a fix had been deployed and that teams were monitoring the platform to ensure services were fully restored. However, the company did not provide details about the root cause of the disruption or explain what led to the interruption in trading activity.

Coinbase shares (COIN) fell sharply during Wednesday’s trading session. As of this writing, the stock is trading at $140, marking an 8% decline over the past several hours. The drop comes as analysts prepare for what many expect to be a challenging fourth‐quarter report.

The daily chart shows COIN’s valuation drop. Source: COIN on TradingView.com

$120 Price Target Issued For COIN

Research firm Monness Crespi took a notably cautious stance ahead of the earnings announcement. The firm issued a double downgrade on Coinbase stock, moving its rating from buy to sell.

Analyst Gus Gala described earlier expectations of a steady recovery through 2026 as “foolish,” citing the historical depth and duration of crypto bear markets.

Monness Crespi now anticipates continued weakness through the first half of the year and has revised its 2026 and 2027 projections to levels below Wall Street consensus estimates. Gala also set a $120 price target for the stock, suggesting that more attractive entry points may emerge later.

Featured image from OpenArt, chart from TradingView.com

Preguntas relacionadas

QWhat was the main issue that Coinbase users experienced according to the article?

ACoinbase users were temporarily unable to buy, sell, or transfer digital assets on the platform due to an unexpected service disruption.

QWhat action did Coinbase take to address the service disruption?

ACoinbase acknowledged the issue on X, investigated the problem, deployed a fix, and had teams monitoring the platform to ensure services were fully restored.

QHow did the service disruption affect Coinbase's stock (COIN) price?

ACoinbase shares fell sharply, trading at $140 and marking an 8% decline during Wednesday's trading session.

QWhich research firm issued a double downgrade and a $120 price target for Coinbase stock?

AResearch firm Monness Crespi issued a double downgrade, moving its rating from buy to sell, and set a $120 price target for the stock.

QWhat reason did analyst Gus Gala give for the cautious stance on Coinbase's recovery expectations?

AAnalyst Gus Gala described earlier expectations of a steady recovery as 'foolish,' citing the historical depth and duration of crypto bear markets.

Lecturas Relacionadas

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.

bitcoinistHace 8 min(s)

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHace 8 min(s)

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.

marsbitHace 8 min(s)

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

marsbitHace 8 min(s)

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.

marsbitHace 13 min(s)

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

marsbitHace 13 min(s)

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.

marsbitHace 58 min(s)

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

marsbitHace 58 min(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

Trading

Spot