Crypto Industry Bands Together To Demand Clear Betting Market Laws

bitcoinistОпубліковано о 2026-02-18Востаннє оновлено о 2026-02-18

Анотація

A new advocacy effort led by The Digital Chamber’s Prediction Markets Working Group is pushing for clearer federal regulation of crypto prediction markets in the U.S. The group supports the CFTC’s stance that such markets should fall under federal oversight rather than state-level enforcement. Recent legal actions highlight the tension: state regulators, like in Nevada, are treating these platforms as unlicensed gambling operations, while companies like Polymarket and Kalshi argue they offer derivative-like contracts under federal jurisdiction. The conflict is moving through courts, with both sides preparing legal arguments, and future regulatory proposals may shape the outcome.

A new, organized push is under way to shape how crypto prediction markets are treated in the US. A blockchain advocacy group has launched a unit aimed at guiding policy, pressing regulators, and backing industry players through legal fights and public research.

Industry Sets Legal Strategy

According to the group’s announcement, the first move was a letter praising the Commodity Futures Trading Commission and its chair for arguing that federal oversight should cover many event contracts.

The Prediction Markets Working Group, created by the blockchain advocacy group, The Digital Chamber, called for clearer rules and an end to what it described as enforcement-first regulation.

The group plans to meet with regulators, file policy ideas, publish studies and join court fights through friend-of-the-court briefs to press its view that a single federal regulator should be the lead voice on these crypto markets.

The regulator’s recent public comments were framed as support for that approach. CFTC Chairman Mike Selig has said the agency has overseen similar contracts for many years, and industry backers see that as a foundation for wider federal authority.

4/4 Focusing exclusively on shaping durable and responsible policy and regulation, our Prediction Markets working group looks forward to working closely with the CFTC, Congress, and market participants. Full statement: https://t.co/p9T7pP7e6r

— The Digital Chamber (@DigitalChamber) February 17, 2026

Tests On The Ground

Reports note that litigation and enforcement are already testing the theory. A major crypto US platform was hit with state action this week, accused of offering unlicensed wagering.

Kalshi faces a civil case brought by a state gaming regulator seeking to stop certain markets that the regulator calls gambling.

Rival platforms have felt the squeeze too; one has moved to federal court to try to head off state bans. Polymarket sued a state to argue federal oversight takes precedence.

The platforms argue their contracts behave like derivatives and should be treated as such, while state officials keep saying these products look a lot like bets.

As of today, the market cap of cryptocurrencies stood at $2.31 trillion. Chart: TradingView

States Push Back

That tension is clear along state lines. Nevada Gaming Control Board, which enforces strict gambling rules in its jurisdiction, has been among the most aggressive.

Reports say a governor in another state called these markets gambling that harms people, signaling political heat. Utah Governor Spencer Cox criticized federal arguments and framed the issue as one of public safety.

Meanwhile a platform chose to take its fight to the federal courts in a state that has been moving toward enforcement. Massachusetts figures into that legal push.

Image: Flowcarbon

What Comes Next

The next stretch will likely be shaped by filings and court rulings as much as by rulemaking. Industry lawyers are preparing to press federal primacy; state officials are planning to press their gambling statutes.

Legal briefs and amicus filings will try to persuade judges about what these crypto contracts really are. Regulators could also respond with formal rule proposals, and those would change the tone of the debate.

Featured image from The Center for Public Justice, chart from TradingView

Трендові криптовалюти

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

PancakeSwapCAKE

JUSTJST

Пов'язані питання

QWhat is the main goal of the newly formed Prediction Markets Working Group?

AThe main goal is to guide policy, press regulators, and back industry players through legal fights and public research to advocate for clearer rules and a single federal regulator for crypto prediction markets.

QWhich US regulatory body did the working group praise in its first move, and why?

AThe working group praised the Commodity Futures Trading Commission (CFTC) and its chair for arguing that federal oversight should cover many event contracts, seeing it as a foundation for wider federal authority.

QWhat legal action did the state of Nevada take against crypto prediction markets, according to the article?

AThe Nevada Gaming Control Board, which enforces strict gambling rules, has been among the most aggressive in taking action against these markets, with one major US platform facing a civil case from a state gaming regulator.

QHow are crypto prediction market platforms arguing their products should be classified and regulated?

AThe platforms argue that their contracts behave like derivatives and should be treated as such under federal oversight, rather than being classified as gambling and regulated by state laws.

QWhat is the current market capitalization of cryptocurrencies as mentioned in the article?

AAs of the date of the article, the market capitalization of cryptocurrencies stood at $2.31 trillion.

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