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AI Agents Transform Arbitrage Dynamics in Prediction Markets

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Prediction markets, built to aggregate collective judgment, are increasingly being overshadowed by ultra-fast automated systems that can exploit fleeting pricing gaps in real time. As artificial intelligence-driven agents begin to operate at scale, the window for profit from mispricings is narrowing for human traders and expanding for algorithmic traders capable of scanning thousands of markets per second.

According to Rodrigo Coelho, CEO of Edge & Node, the current landscape already favors automated execution: bots are scanning hundreds of markets every second, and AI-driven agents are poised to expand their role as these capabilities mature. “Capturing those opportunities requires monitoring thousands of markets and executing trades almost instantly, which is why they’re largely dominated by automated systems,” Coelho told Cointelegraph. He added that prediction markets are a natural next step for AI systems designed to exploit short-lived pricing gaps without human input.

That view aligns with broader observations about how prediction markets operate in practice. While participants can speculate on outcomes independent of macro conditions, the fastest arbitrageurs—often automated—can lock in profits from tiny deltas in probability. As one observer noted, even a several-second delay between an event and a market update can create a latency arbitrage opportunity that bots can monetize with near certainty in that brief window.

In recent years, researchers have documented consistent pricing inefficiencies in prediction markets. A study examining Polymarket found frequent mispricings within individual markets and across related markets, enabling arbitrage positions. The researchers estimated that roughly $40 million had been extracted from these inefficiencies, illustrating the real monetary potential of such mispricings when exploited at scale. These findings underscore why the space is proving attractive to automation enthusiasts and AI researchers alike.

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Prediction markets are still nascent, but their underlying technology is evolving. Polymarket, for example, has taken steps to bolster trading costs and reduce immediate profitability for certain strategies by introducing taker fees in shorter-duration markets. Outcomes are not finalized instantly, which tempers the reliability of some arbitrage approaches and complicates the profitability math for participants.

Key takeaways

  • Latency arbitrage in prediction markets creates near-term edge opportunities that are most easily exploited by automated trading systems scanning thousands of markets per second.
  • A recent academic study suggests Polymarket exhibits persistent pricing inefficiencies, with researchers estimating roughly $40 million extracted from arbitrage opportunities.
  • Open interest in Polymarket surged during the 2024 U.S. elections, reflecting ongoing appetite for prediction-market exposure, with politics, sports, and crypto among the most-active topics.
  • As AI agents grow more capable, concerns about market manipulation rise, including the potential for large capital holders to sway outcomes in thin markets.
  • The transition from simple execution bots to autonomous, AI-assisted trading systems could broaden participation but also heighten the need for guardrails and prudent oversight.

Latency, mispricings, and the economics of prediction markets

The core economics of prediction markets hinge on price discovery and the accuracy of probabilities assigned to outcomes. When a participant or an algorithm can detect an event and respond faster than the market can recalibrate, a temporary mispricing can appear. In practice, even a few seconds of delay can offer a window in which an automated trader guarantees a favorable outcome, provided the market update occurs belatedly after the event realization.

Academic work and industry observations converge on a similar point: mispricings are not rare in practice, and the profitability of exploiting them is highly sensitive to speed and information latency. Polymarket’s own market design and liquidity dynamics contribute to such inefficiencies, particularly in markets with lower liquidity or where probability sums do not align perfectly across related instruments. The estimated $40 million extracted from arbitrage underscores the materiality of these opportunities, even as total trading volumes grow and platforms attempt to tighten pricing frictions.

These dynamics are amplified by the evolving technical toolkit behind trading. On the one hand, humans continue to participate and conduct analyses using conversational AI and data tooling. On the other hand, a growing cadre of automated agents can operate with minimal human input, allowing them to act on micro-second or second-level signals that might elicit only modest reactions from human traders.

AI agents, governance, and the risk of influence in thin markets

Beyond pure arbitrage, AI agents raise governance questions about how markets respond to large-scale automated activity. Large players with substantial capital can influence outcomes by concentrating bets on a single side, a dynamic that has sparked fresh concerns about manipulation as AI agents gain sophistication. In one high-profile reference, a Bloomberg report described a prominent incident during an election cycle in which a large, unidentified trader placed a multi-million-dollar bet on a specific political outcome, highlighting how sizable wagers can tilt sentiment in prediction markets when liquidity is thin.

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Data from Dune Analytics shows Polymarket’s open interest peaked around the 2024 U.S. elections, with politics remaining the dominant topic and sports and crypto rounding out the top categories. The evolution of open interest signals sustained engagement in a speculative tool that, at scale, can be swayed by large bets and rapid shifts in funding. As AI agents become more capable of pattern recognition and decision-making, the stakes for responsible market design and guardrails rise accordingly.

Industry observers emphasize that this is not a purely hypothetical concern. Pranav Maheshwari, an engineer at Edge & Node, argues that the increasing capability of AI agents makes guardrails essential as these systems begin acting autonomously at scale. “With higher capabilities, you need to restrict permissions and ensure safety measures to prevent unintended consequences,” he noted. The sentiment is echoed across the field: as agents move from assisting with research to executing trades and policies autonomously, the potential for unintended market impacts grows.

Polymarket’s own evolution illustrates the tension between accessibility and risk. While the platform has lowered barriers for users and introduced measures such as taker fees to temper aggressive short-horizon trading, final outcomes still require human or semi-automated oversight. The presence of AI-enabled strategies in this space highlights a broader question for regulators and platform designers: how to preserve market integrity and prevent manipulation while encouraging innovation and participation.

From execution bots to autonomous trading: the broader industry shift

Market participants are increasingly observing a shift in how trading is conducted. The early generation of arbitrage relied on rule-based bots designed for fast execution, but the frontier now extends to AI-assisted systems that can identify opportunities in real time, interpret structured data, and autonomously decide on trades. Industry voices note that many retail traders still rely on research interfaces and chat-based tools for decision support, but the most advanced users are experimenting with automated policies and even autonomous trading agents.

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Archie Chaudhury, CEO of LayerLens, describes a spectrum of activity: a portion of retail participants use coding agents to create automated bots or algorithms, while others pursue higher levels of automation that can broadcast or enforce trading policies. He also notes that large language models are well-suited to parsing and interpreting financial data, potentially lowering the technical barriers that historically separated retail and institutional-grade quantitative activity. The result is a trading ecosystem where execution speed and data interpretation power increasingly determine competitive advantage.

Despite the rapid progression, the market remains highly dependent on the quality of the underlying data and the reliability of the pricing mechanisms. As automation becomes more prevalent, traders and platforms alike will need to balance the drive for speed with safeguards that prevent manipulation and preserve fair access for participants with varying levels of technical sophistication.

Looking ahead, the trajectory suggests two intertwined themes: the continued improvement of AI agents and the ongoing maturation of governance frameworks around prediction markets. The acceleration of autonomous decision-making poses opportunities for more efficient price discovery and broader participation, but it also raises questions about transparency, accountability, and the risk of concentrated influence in thin markets.

For investors and builders, the takeaway is clear: expect the edge to shift from human reaction time to automation and data-driven decision-making. Platform designers should prioritize robust risk controls, explicit permissioning for autonomous agents, and clearer disclosure around open-interest dynamics and pricing inefficiencies. Regulators, meanwhile, will weigh how to preserve market integrity without stifling innovation in this rapidly evolving sector.

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As AI literacy among retail participants grows, the ecosystem will likely see a wider adoption of automated tools, alongside ongoing debates about guardrails and oversight. The coming quarters will reveal how much of the current arbitrage edge can be sustained as markets and technologies evolve in tandem.

What remains uncertain is how quickly regulatory frameworks will adapt to these capabilities and what new guardrails will emerge to balance openness with protection against manipulation. Investors and traders should monitor policy developments, platform responses to latency risks, and the emergence of standardized practices for autonomous trading in prediction markets.

Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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Bitcoin Breakout Attempt Fails as Rejection at Resistance Opens Door to $63K Revisit

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • Bitcoin failed to hold above key resistance after a retest, signaling a classic rejection pattern for BTC.
  • Analyst Dami-Defi warns that rallies below the yellow line are relief bounces with the $63K zone as next target.
  • Coinbase continues selling into every bounce while spot inflows from institutions remain notably absent.
  • A weekly close below $68K could confirm deeper downside, with analysts eyeing the $55K to $60K range.

Bitcoin’s price action is drawing serious attention after a failed retest at a key resistance level. The rejection has shifted market sentiment toward the downside.

Analysts are now pointing to the $63,000 demand zone as the next probable target. With no confirmed breakout and weak institutional inflows, the path of least resistance appears to trend lower in the near term.

Failed Retest at Key Resistance Puts $63K in Focus

Bitcoin broke above a critical resistance level but could not sustain the move. The price returned to retest that level and was firmly rejected.

Analyst Dami-Defi flagged this as a textbook breakout attempt followed by retest and rejection. That sequence historically points toward a return into the previous trading range.

Dami-Defi described the behavior as anything but confirmed breakout price action. A legitimate breakout holds above the broken level after the retest occurs.

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The failure to do so hands control back to the bears. He maintained a straightforward stance: bearish until the chart proves otherwise on closes.

With BTC trading below the yellow resistance line, rallies carry little conviction. Dami-Defi characterized any upward moves as relief bounces rather than trend reversals.

The $63,000 base, marked as a gray demand zone, now serves as the next key magnet. That area represents where buyers previously stepped in with enough force to matter.

Should that $63,000 zone fail to hold on real closes, the analyst warned of further downside. A clean break below it would shift the chart toward a deeper correction scenario.

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Traders are encouraged to focus on closing prices rather than short-term wicks. The rejection at resistance remains the clearest signal guiding this outlook.

Institutional Selling and Macro Weakness Reinforce Downside Risks

Higher timeframe analysis from analyst Junar adds another layer to the bearish case. He pointed out that Bitcoin lost the critical 72,500 level on the higher timeframe chart.

That loss carries weight because it reflects a structural shift in bullish momentum. A reclaim above that level would be needed to revive any serious push toward $79,000.

Until then, Junar noted that Coinbase continues selling into every bounce. Spot inflows from institutional players remain absent at current price levels.

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That dynamic limits buying pressure and keeps the market vulnerable to further slippage. Choppy price action is expected to persist over the coming weeks as a result.

A weekly close below $68,000 would serve as the next major warning for traders. Junar identified that level as separating a consolidation phase from a genuine breakdown.

Losing it on closes puts $60,000 squarely in view as the following target. He advised traders to consider building positions gradually in the $55,000 to $60,000 range.

Junar also urged market participants to tune out overly optimistic narratives currently circulating online. Swing trades carry elevated risk under these conditions, making scalping the more practical approach. Until a clear directional shift emerges, patience remains the most disciplined strategy available.

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Apple Updates Siri with Gemini to Power Next-Gen AI Features

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • Apple Gemini Siri update integrates Google Gemini, shifting Apple toward external AI models for advanced capabilities.
  • Rising AI training costs make in-house model development less efficient, pushing firms toward partnerships.
  • Apple retains control over UX, distribution, and privacy while relying on Google for the AI model layer.
  • The move signals a broader industry trend where foundation models become concentrated among a few providers.

Apple Gemini Siri update signals a shift in Apple Inc.’s approach to artificial intelligence as it integrates Google’s Gemini into its voice assistant.

This move reflects changing economics in AI development and a broader industry shift toward shared model infrastructure.

Apple Gemini Siri Update and AI Economics

Apple’s update is shaped by the rising cost of training frontier AI systems. Modern large-scale models require extensive computing resources, proprietary datasets, and continuous retraining cycles. 

These demands have made independent model development less cost-efficient, even for large firms.

Reports indicate Apple will license Google’s Gemini model, which is described as a 1.2 trillion-parameter system. The arrangement is expected to cost around $1 billion annually. 

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This approach allows Apple to access advanced capabilities without committing to full-scale model training infrastructure.

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The updated Siri, expected in iOS 26.4, will handle complex tasks such as summarization, planning, and contextual responses. It will also include on-screen awareness, allowing interaction across apps. 

A post shared in tech discussions noted, “AI now sits between the user and the system, not just as a feature.”

Apple is positioning this as a transitional approach. While using external models for immediate performance, it continues to invest in internal AI development. 

This dual strategy allows Apple to remain competitive while managing costs and development timelines.

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Ecosystem Control and Strategic Positioning

Gemini Siri update also highlights Apple’s focus on ecosystem control. The company retains authority over hardware, operating system, and user interface, while outsourcing the model layer. 

This ensures that the user experience remains tightly integrated within Apple’s ecosystem. The system will run through Apple’s Private Cloud Compute infrastructure, which supports its privacy framework. 

This approach allows Apple to maintain its emphasis on data protection while still leveraging advanced external AI capabilities.

Apple continues to focus on distribution strength, with over a billion active devices worldwide. By integrating Gemini into Siri, Apple ensures that AI becomes a native part of the user interface rather than a separate tool.

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A widely circulated comment summarized the shift: “The interface layer now defines the AI experience more than the model itself.” 

This reflects Apple’s positioning strategy, where control of user interaction takes priority over ownership of the underlying model.

At the same time, Apple’s reliance on Google introduces a degree of dependency. This could influence future development timelines and feature evolution. 

However, Apple’s internal AI work suggests that this partnership is not permanent, but rather part of a staged transition.

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Apple Gemini Siri update, therefore, represents a measured shift in strategy, balancing external partnerships with long-term internal development goals.

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Who Owns the Most Bitcoin in 2026? Arkham Data Reveals Top Holders

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • Satoshi Nakamoto holds 1.096 million BTC worth $77B, making him the largest Bitcoin holder globally.
  • Coinbase controls 5% of Bitcoin’s total supply, leading all exchanges with 982,000 BTC in holdings.
  • The U.S. Government holds 328,000 BTC seized from Bitfinex, Silk Road, and the LuBian Hacker address.
  • Strategy holds 738,000 BTC total, making it the largest public company Bitcoin holder as of 2026. 

Bitcoin ownership remains concentrated among a select group of entities as of 2026. On-chain data from Arkham Intelligence reveals that Satoshi Nakamoto holds the largest known share.

Exchanges, ETF issuers, and governments follow closely behind. Public companies like Strategy have also accumulated substantial reserves over the past few years.

The data provides a clear picture of where the world’s most valuable digital asset resides today, and who holds the most of it.

Satoshi Nakamoto Leads All Bitcoin Holders Worldwide

Satoshi Nakamoto, the pseudonymous creator of Bitcoin, remains the single largest known holder. Arkham’s research attributes 1.096 million BTC to Satoshi, worth approximately $77 billion. This figure rests on a known mining pattern called the Patoshi Pattern.

Arkham’s data links these holdings to around 22,000 blocks that Satoshi mined in the network’s early days. The identified addresses include the only known wallets from which Satoshi ever spent BTC. No movement has been recorded from most of these wallets in years.

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Among individual wallet addresses, a Binance cold wallet holds the most BTC. That single address contains nearly 250,000 BTC, worth around $17 billion. It ranks as the largest single-address Bitcoin wallet currently on record.

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Exchanges and ETF Issuers Command Billions in Holdings

Coinbase is the largest exchange entity by BTC holdings, controlling around 982,000 BTC. That figure represents roughly 5% of Bitcoin’s total circulating supply. Binance follows with approximately 655,000 BTC, equal to 3.3% of supply.

BlackRock leads all ETF issuers with 775,000 BTC held under its spot Bitcoin ETF. Fidelity Custody holds 460,000 BTC, while Grayscale, Bitwise, and ARK Invest also maintain on-chain positions. Arkham first identified these ETF holdings on-chain after the products launched in the U.S. in January 2024.

Grayscale’s Bitcoin holdings are spread across more than 1,750 separate addresses. Each address holds no more than 1,000 BTC. All assets are custodied through Coinbase.

Governments Hold Bitcoin Largely Through Criminal Asset Seizures

The United States Government holds 328,000 BTC, making it the top government holder by a wide margin. These holdings come from seizures tied to the Bitfinex hack, Silk Road, and the LuBian Hacker address. The FBI manages these wallets on behalf of the federal government.

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The United Kingdom holds 61,245 BTC, seized from Jian Wen and Zhimin Qian in 2018. El Salvador holds 7,500 BTC, accumulated through daily purchases and a legal tender policy. Bhutan holds 5,400 BTC, mined through its sovereign wealth fund using hydroelectric power.

Unlike seizure-based holdings, El Salvador and Bhutan acquired Bitcoin through active national strategies. El Salvador adopted it as legal tender and bought 1 BTC daily under President Bukele’s directive. Bhutan partnered with Bitdeer to expand mining operations backed by cheap hydroelectric energy.

Public and Private Companies Continue Accumulating BTC Reserves

Strategy, formerly MicroStrategy, holds more Bitcoin than any other public company. Its total holdings stand at 738,000 BTC, though on-chain data confirms 443,000 BTC directly. The company has been buying consistently since August 2020.

MARA, a publicly traded mining company, reports a treasury stockpile of 53,200 BTC. Metaplanet, listed in Tokyo, holds 35,100 BTC as a hedge against yen depreciation. Both companies closely mirror Strategy’s long-term accumulation approach.

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Among private companies, Tether holds 96,300 BTC verified on-chain. SpaceX holds 8,300 BTC, down from a peak of 28,000 BTC in 2021. Block.one claims 164,000 BTC, though those holdings remain unverified through on-chain data.

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Hyperliquid Hits Net Deflation as HyperCore Buybacks Exceed Daily Staking Rewards

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • HyperCore repurchased 34,495.71 HYPE at $38.51 on March 27, exceeding daily staking distributions.
  • A net 7,711 HYPE were permanently removed from circulation, projecting to 2.77M tokens yearly.
  • Unlike Solana’s 25.19M annual inflation, Hyperliquid is actively reducing its total token supply.
  • Higher HIP-3 adoption drives more revenue, fueling larger buybacks and compounding deflation pressure.

Hyperliquid recorded net deflation on March 27, 2026, as HyperCore repurchased more HYPE tokens than it distributed.

The buyback totaled 34,495.71 HYPE at an average price of $38.51. Against 26,784 HYPE paid out to stakers and validators, the net removal stood at 7,711 tokens.

This marks a notable shift in how the protocol manages its circulating supply.

Buyback Activity Drives Daily Supply Reduction

On March 27, HyperCore’s repurchase program pulled 34,495.71 HYPE from circulation. The distribution of 26,784 HYPE went to stakers and 24 active validators on the same day. After accounting for both figures, 7,711 HYPE were permanently removed from supply.

At this pace, the monthly net reduction reaches approximately 231,330 HYPE. Annually, that projects to nearly 2,775,960 HYPE taken out of circulation. These numbers reflect a consistent deflationary trend rather than a one-time event.

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According to Hyperliquid Hub, the buyback mechanism also responds to price movement. When HYPE trades higher, fewer tokens are repurchased per dollar spent. When prices fall, the protocol buys back more aggressively, which naturally manages supply pressure.

Protocol Revenue Feeds a Self-Reinforcing Cycle

The deflation model ties directly to trading activity on the network. More adoption of HIP-3 leads to higher trading volumes across the platform. That activity generates greater protocol revenue, which then funds larger buyback operations.

As Hyperliquid Hub noted, this creates a flywheel: “More HIP-3 adoption → higher trading activity → more protocol revenue → larger buybacks.”

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Each component reinforces the next without requiring external intervention. The system is built to scale its deflationary pressure alongside usage.

For context, Solana issues roughly 25.19 million SOL annually through its staking and validator reward structure. Hyperliquid, by contrast, is removing more tokens than it issues on a daily basis. The two networks represent opposite ends of the supply management spectrum.

The price-sensitive nature of the buyback adds another layer of stability to the model. It functions as a built-in counter to extreme market swings in either direction. Over time, this structure may reduce volatility tied to supply-side selling pressure.

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Kalshi Hit With Washington State Lawsuit

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Kalshi Hit With Washington State Lawsuit

Kalshi is facing another state-level lawsuit after the state of Washington on Friday filed allegations that the prediction market operator violated state gambling laws with its products.

The Washington Attorney General’s complaint cites the Pacific Northwest state’s existing ban on online gambling and otherwise strict oversight of the gaming market, in claiming Kalshi violated the Washington Consumer Protection Act, Gambling Act, and Recovery of Money Lost at Gambling Act.

“Kalshi’s website and app show consumers a range of events that they can bet on and the odds for those various events, which dictate how much the bettor will be paid out if the event occurs,” an announcement from Attorney General Nick Brown said. “This is exactly how sportsbooks and other gambling operations function. Kalshi advertises that they allow consumers to ‘bet on anything’ by simply calling their service a ‘prediction market’ rather than ‘gambling.’”

The definition of gambling under Washington law is “staking or risking something of value upon the outcome of a contest of chance or a future contingent event,” and Kalshi’s activities fall squarely within that definition, the AG’s announcement said. “Each Kalshi bet risks money, relies in part on chance, and promises a payout to winners.”

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Kalshi immediately sought to move the case to federal court, saying in its filing that the issues raised by the Washington suit are already being  litigated in other federal courts and that there had been “no warning or dialogue” from Washington state  prior to the lawsuit.

Related: SEC interpretation on crypto laws ‘a beginning, not an end,‘ says Atkins

Cover page of State of Washington v. KalshiEx, Source: King County Superior Court

State AGs and gaming regulators mount legal fights across the country

A Nevada judge earlier this month temporarily blocked Kalshi from operating in the state, finding that state authorities are reasonably likely to prevail in a legal fight over whether the company’s event contracts violate Nevada gambling laws.

Carson City District Court Judge Jason Woodbury issued a temporary restraining order on Friday, siding with a Nevada Gaming Control Board motion to block Kalshi from operating in the state for 14 days.

Kalshi had argued that its contracts are under the exclusive jurisdiction of the US Commodity Futures Trading Commission, an agency that has backed prediction markets that are fighting in multiple state courts over accusations of offering illegal gambling.

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Days earlier, Arizona Attorney General Kris Mayes announced charges against the companies behind Kalshi, alleging that the company operated an “illegal gambling business in Arizona without a license” and offered illegal election wagering.

While Kalshi faces several similar cases filed by gaming authorities in other US states over the platform allegedly offering sports gambling to residents without a license, Arizona was one of the first to file criminal charges.

The state-level cases come as prediction markets are under scrutiny by lawmakers for offering bets on US military actions, citing concerns about insider information in the government.

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