Phantom taps Kalshi to offer regulated prediction markets in wallet

cointelegraphPublished on 2025-12-12Last updated on 2025-12-12

Abstract

Cryptocurrency wallet Phantom has partnered with regulated prediction market Kalshi to integrate event-based trading directly into its wallet interface. The new feature, Phantom Prediction Markets, will allow users to trade tokenized positions on real-world events in politics, economics, sports, and culture without leaving the app. This move aligns with a broader trend, as major crypto exchanges like Gemini and Coinbase are also entering the prediction market space. However, the industry faces regulatory challenges, exemplified by Connecticut's recent cease-and-desist orders against several platforms, including Kalshi, which is now legally challenging the state's actions.

Crypto wallet application Phantom has partnered with regulated prediction market Kalshi to bring event-based trading directly inside its wallet interface, signaling a deeper convergence between onchain finance and real-world outcome betting.

The companies said Friday that the integration would allow Phantom users to discover trending events, track live odds and place bets without leaving their wallets.

A new feature called Phantom Prediction Markets would allow users to trade tokenized positions that reference Kalshi’s event markets across politics, economics, sports and culture.

“By integrating a layer of tokenized positions referencing Kalshi’s regulated event markets with Phantom, users can trade what they care about in real time,” said Phantom CEO Brandon Millman.

Source: Phantom

Crypto exchanges eye US prediction markets

Phantom’s move comes as major crypto trading platforms race to enter the US prediction markets business.

On Thursday, Gemini Titan, an affiliate of the crypto exchange Gemini, received a designated contract market license from the US Commodity Futures Trading Commission (CFTC). Gemini said it plans to enter the prediction markets space.

The exchange said that it would allow users to access event contract trading on its web platform. Following its announcement, Gemini shares went up by nearly 14% in after-hours trading.

On Nov. 19, tech researcher Jane Manchun Wong, known for discovering in-development features on Big Tech websites, claimed that crypto exchange Coinbase is working on a prediction market. Wong shared screenshots apparently showing the unreleased platform.

Citing anonymous sources, Bloomberg reported that Coinbase plans to announce the launch of its prediction markets and tokenized equities.

A Coinbase spokesperson previously told Cointelegraph that they company will hold a livestream on Wednesday to showcase new products. However, the spokesperson did not mention prediction markets or tokenized stocks.

Related: Polymarket trading figures are being double-counted: Paradigm

Prediction markets face regulatory pushback

While prediction markets have gained popularity in the US, the state of Connecticut has recently taken a stance against certain platforms.

On Dec. 4, the Connecticut Department of Consumer Protection (DCP) sent cease and desist orders to Robinhood, Kalshi and Crypto.com, alleging that they were conducting unlicensed online gambling. However, Kalshi immediately took action a day later.

The prediction market platform sued the DCP, arguing that its event contracts are lawful under federal law.

Connecticut federal court judge Vernon Oliver stated in an order that the DCP must refrain from taking enforcement action against Kalshi. This temporarily stops the DCP’s cease and desist order against Kalshi.

Magazine: Koreans ‘pump’ alts after Upbit hack, China BTC mining surge: Asia Express

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit48m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit48m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit53m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit53m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit53m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit53m ago

Trading

Spot
活动图片