Federal Ruling Raises Risk for Polymarket, Kalshi in Nevada

TheNewsCryptoPubblicato 2026-03-03Pubblicato ultima volta 2026-03-03

Introduzione

A US federal court ruling has increased the risk that Nevada regulators may halt prediction-market trading in the state. The court rejected arguments that federal commodity regulations preempt state gaming laws, allowing the Gaming Control Board to pursue its case against Polymarket’s parent company, Blockratize, in state court. This could lead to an injunction preventing Nevada residents from accessing event contracts offered by Polymarket or Kalshi. The decision comes amid growing regulatory pressure on prediction markets, with Kalshi also facing a similar state-level enforcement action. Legal experts suggest the ruling may encourage other states to seek injunctions against event contracts.

The US federal court ruling has pushed the risk even higher that Nevada regulators could look for halting prediction-market trading in the state after a judge sent a contest comprising Polymarket’s parent company, Blockratize, back to state court.

A federal judge declined arguments that US regulation under the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) completely prevents state gaming laws for prediction markets, as per a Monday order.

The judge found that the savings clause of the CEA does not fully replace state authority and that the firms had not revealed a basis to obstruct the action of Nevada at this stage. The decision shows the Nevada Gaming Control Board can carry on going after its civil enforcement case in state court, where it could look for an injunction limiting Nevada residents from accessing event contracts provided by Polymarket or Kalshi.

Decision Amid The Increasing Pressure

With regards to the ruling, the parent company of Polymarket submitted a motion to request a brief administrative stay of the remand order of the court. The motion is a legal request looking to freeze a court ruling or enforcement action witnessed as a short-term emergency measure.

The Nevada decision comes amid the increasing pressure on prediction markets from state regulators, including Kalshi, which has been fighting Nevada’s gaming regulator since 2025.

A federal judge on March 3 also remanded the civil enforcement action of Nevada against Kalshi back to the state court, exposing Kalshi to an imminent temporary restraining order excluding it from providing event contracts in the state, as per a court filing witnessed from sports betting and gaming-aimed lawyer Daniel Wallach.

Today, Wallach posted on X mentioning that the ruling could encourage other states to sue Kalshi in state court and look for injunctions to hinder event contracts, a strategy that has already succeeded in every case brought.

Highlighted Crypto News Today:

Aptos (APT) Charts Turn Bullish: Is a $1 Surge Loading?

Domande pertinenti

QWhat is the main implication of the federal court ruling for Polymarket and Kalshi in Nevada?

AThe ruling increases the risk that Nevada regulators could halt prediction-market trading in the state, as it allows the Nevada Gaming Control Board to pursue its civil enforcement case in state court and potentially seek injunctions to restrict Nevada residents from accessing event contracts offered by Polymarket or Kalshi.

QWhich federal laws did the judge rule do not completely preempt state gaming laws for prediction markets?

AThe judge ruled that the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) regulations do not completely preempt state gaming laws for prediction markets, as the savings clause of the CEA does not fully replace state authority.

QWhat action did Polymarket's parent company take in response to the court's remand order?

APolymarket's parent company, Blockratize, submitted a motion requesting a brief administrative stay of the court's remand order, which is a legal request to freeze the ruling or enforcement action as a short-term emergency measure.

QHow has the ruling impacted Kalshi specifically, according to the article?

AThe ruling exposed Kalshi to an imminent temporary restraining order that would exclude it from providing event contracts in Nevada, as a federal judge also remanded Nevada's civil enforcement action against Kalshi back to state court on March 3.

QWhat broader consequence might this ruling have for prediction markets in other US states, as suggested by lawyer Daniel Wallach?

ADaniel Wallach suggested that the ruling could encourage other states to sue Kalshi in state court and seek injunctions to hinder event contracts, a strategy that has already succeeded in every case brought.

Letture associate

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit10 min fa

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit10 min fa

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbit16 min fa

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbit16 min fa

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit16 min fa

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit16 min fa

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2 h fa

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2 h fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit2 h fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit2 h fa

Trading

Spot

Futures