Prediction Market Clash: CFTC Sues Three States To Claim Exclusive Control

bitcoinistОпубліковано о 2026-04-03Востаннє оновлено о 2026-04-03

Анотація

The US Commodity Futures Trading Commission (CFTC) has filed lawsuits against Arizona, Connecticut, and Illinois, including Illinois Governor J.B. Pritzker, to assert exclusive federal authority over prediction markets. The CFTC argues that event contracts on platforms like Kalshi and Polymarket fall under its jurisdiction under the Commodity Exchange Act, and that state-level regulations create a fragmented system that increases risks of fraud and market abuse. This legal action reflects growing tension between federal and state regulators. Meanwhile, Congress is considering legislation to ban prediction markets on sensitive topics like elections and wars, and the NFL has requested operators block certain sports-related event contracts. The CFTC has also initiated a rulemaking process to clarify and reinforce its regulatory role over these markets.

The US Commodity Futures Trading Commission (CFTC) has escalated a jurisdictional clash with state governments by filing lawsuits against three states in a bid to assert exclusive federal authority over prediction markets.

The litigation targets Arizona, Connecticut, and Illinois — and in Illinois’ case, specifically names Governor J.B. Pritzker — after those states took steps the CFTC says improperly constrain or try to regulate contract markets that are registered with the agency.

CFTC Seeks Unified Regulation

In a statement announcing the legal action, the CFTC said event contracts traded on platforms such as Kalshi and Polymarket fall squarely within the Commission’s remit under the Commodity Exchange Act.

The agency argued that Congress intentionally established a unified national regulatory framework for commodity derivatives markets to prevent a fragmented patchwork of state rules that would, in the regulator’s view, undermine consumer protection and increase risks of fraud and manipulation.

“The CFTC will continue to safeguard its exclusive regulatory authority over these markets and defend market participants against overzealous state regulators,” CFTC Chairman Mike Selig said in the release.

The suits mark the first time the regulator has resorted to litigation to press this point, reflecting mounting tension between federal and state officials over how to treat prediction markets.

Congress Considers Tighter Prediction‐Market Curbs

The CFTC accused the named states of attempts to outlaw, limit, or otherwise interfere with the operations of designated contract markets (DCMs) that are registered with the Commission.

Those state actions, the agency said, run contrary to the Commodity Exchange Act’s delegations and risk imposing inconsistent obligations on market participants.

The regulator noted it recently issued an Advanced Notice of Proposed Rulemaking to clarify the application of the CEA and CFTC regulations to prediction markets, and signaled it expects to follow through with formal rulemaking that will more explicitly define and reinforce its supervisory role.

The legal push comes as Capitol Hill and other institutions weigh tighter curbs on certain types of event contracts. A group of congressional Democrats last week introduced legislation that would ban prediction-market wagers on sensitive topics, including elections, war, and sports.

Separately, Massachusetts Representative Seth Moulton proposed a restriction banning congressional staff from using prediction markets, a measure believed to be unprecedented in Congress.

Pressure has also come from professional sports organizations. Sabrina Perel, the National Football League’s (NFL) chief compliance officer, wrote to prediction market operators — in a letter reviewed by CNBC — asking them to block event contracts she considered objectionable.

The NFL has signaled that it believes sports-related contracts may warrant a distinct regulatory approach, an idea that mirrors the CFTC’s position that certain event contracts may need special attention.

The daily chart shows the total crypto market drop below $2.3 trillion on Thursday. Source: TOTAL on TradingView.com

Featured image from OpenArt, chart from TradingView.com

Пов'язані питання

QWhat is the main reason the CFTC is suing the states of Arizona, Connecticut, and Illinois?

AThe CFTC is suing these states because they took steps that the agency claims improperly constrain or try to regulate contract markets that are registered with the CFTC, which the Commission argues violates its exclusive federal authority under the Commodity Exchange Act.

QAccording to the CFTC, what is the purpose of the unified national regulatory framework established by Congress?

AThe CFTC argues that Congress established a unified national regulatory framework to prevent a fragmented patchwork of state rules, which would undermine consumer protection and increase the risks of fraud and market manipulation.

QWhat recent legislative action was taken by congressional Democrats regarding prediction markets?

AA group of congressional Democrats introduced legislation that would ban prediction-market wagers on sensitive topics, including elections, war, and sports.

QWhich other organization, besides Congress, has applied pressure on prediction market operators, and what did they request?

AThe National Football League (NFL), through its chief compliance officer Sabrina Perel, wrote to prediction market operators asking them to block event contracts that the league considered objectionable.

QWhat action did the CFTC recently take to clarify its regulatory role over prediction markets?

AThe CFTC issued an Advanced Notice of Proposed Rulemaking to clarify the application of the Commodity Exchange Act and its regulations to prediction markets, and it signaled it expects to follow through with formal rulemaking to more explicitly define and reinforce its supervisory role.

Пов'язані матеріали

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手17 хв тому

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手17 хв тому

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News2 год тому

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News2 год тому

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4 год тому

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4 год тому

Торгівля

Спот
Ф'ючерси
活动图片