Fairshake Raises $193 Million as Crypto Firms Prepare for 2026 Midterm Elections

TheNewsCryptoPubblicato 2026-01-29Pubblicato ultima volta 2026-01-29

Introduzione

Fairshake, the largest crypto-backed political action committee (PAC), has raised $193 million in preparation for the 2026 U.S. midterm elections. Major contributors include Ripple, Andreessen Horowitz, and Coinbase. The group supports candidates based on their stance toward cryptocurrency, aiming to influence lawmakers to back clear crypto regulations. It operates through affiliated groups supporting both Democrats and Republicans who are crypto-friendly. Key legislative priorities include the stalled Digital Asset Market Clarity Act, which defines regulatory roles and rules for crypto companies. Fairshake is mobilizing early to prevent further delays in crypto legislation, particularly if Democrats gain congressional control, and to protect the industry from becoming a political target. The effort underscores crypto’s growing influence in U.S. politics.

Fairshake, the largest political action committee (PAC) backed by the crypto companies, has build $183 million fund ahead of the 2026 U.S. midterm elections. The crypto industries are once again stepping into U.S. politics. This move shows that crypto has become a permanent force in the U.S. election strategy.

Fairshake’s funding comes from the strong, powerful players in the crypto industry, like Ripple with $25 million, Andreessen Horowitz with $24 million, and Coinbase with $25 million. Nearly $200 million already raised, and Fairshake is closely matching what it spent during the last election cycle.

Fairshake’s Political Stand

Fairshakes basically support the political candidate based on their views on crypto. Its goal is to make the lawmakers who support clear crypto rules and those who don’t. It operates through two major affiliated groups, one which supports the Democrats, friendly to crypto, and the other which supports republicans friendly to crypto. Together, they form a permanent political operation to influence future crypto laws in Congress.

The Republicans have passed one major crypto-related law GENIUS Act. This law established the first U.S. federal framework for stablecoins payment in the U.S. and was supported by both parties. However, the progress has largely stalled beyond that.

The most important unresolved issue is the Digital Asset Market Clarity Act. The bill clearly explains which government agencies regulate crypto and how exchanges should be treated. It also explains what rules the crypto companies must follow. The bill has been delayed due to political disagreements and even disagreements within the crypto industry.

Why Fairshake is preparing early

If democrats takes the control of the congress, then there will be more delays in passing major crypto laws. Stricter rules focused on consumer protection and crypto regulation took a back seat to other political issues. This is the reason why Fairshake is preparing early and raising large amounts of money now.

Fairshake’s main goal is not just winning elections; it’s about making sure crypto does not become an easy target during political fights. Without clarity, companies remain vulnerable to sudden enforcement actions and policy changes. So this move from the Firshake shows that the crypto is now deeply embedded in U.S. politics.

Highlighted Crypto News:

Coinbase Expands Prediction Markets to All 50 U.S. States

Domande pertinenti

QHow much money has Fairshake raised for the 2026 midterm elections, and which companies are the major contributors?

AFairshake has raised $183 million for the 2026 midterm elections. Major contributors include Ripple with $25 million, Andreessen Horowitz with $24 million, and Coinbase with $25 million.

QWhat is the primary goal of Fairshake's political action committee?

AFairshake's primary goal is to support political candidates based on their views on cryptocurrency, aiming to elect lawmakers who support clear crypto regulations and oppose those who do not, ensuring crypto is not an easy target in political fights.

QWhat are the two major crypto-related legislative acts mentioned in the article, and what is their status?

AThe two major crypto-related acts are the GENIUS Act, which established the first U.S. federal framework for stablecoins and was passed with bipartisan support, and the Digital Asset Market Clarity Act, which defines regulatory agencies and rules for crypto exchanges but has been delayed due to political and industry disagreements.

QWhy is Fairshake preparing early for the 2026 elections, according to the article?

AFairshake is preparing early because if Democrats take control of Congress, there may be more delays in passing major crypto laws, with stricter consumer protection rules taking a back seat to other political issues, making early fundraising crucial to influence future crypto legislation.

QHow does Fairshake operate to influence crypto laws in Congress?

AFairshake operates through two major affiliated groups: one supporting Democrats friendly to crypto and another supporting Republicans friendly to crypto, forming a permanent political operation to influence future crypto laws in Congress.

Letture associate

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.

bitcoinist49 min fa

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist49 min fa

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.

marsbit49 min fa

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

marsbit49 min fa

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.

marsbit54 min fa

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

marsbit54 min fa

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 h fa

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

marsbit1 h fa

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 h fa

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

marsbit2 h fa

Trading

Spot