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

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.

marsbit9m ago

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

marsbit9m ago

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.

marsbit2h ago

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

marsbit2h ago

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.

marsbit2h ago

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

marsbit2h ago

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

Alliance Co-founder's Letter to Entrepreneurs: On Cursor's $60 Billion Sale Many aspiring founders see massive exits like Cursor's $60B sale and wonder why they can't achieve the same, often concluding opportunities are exhausted. But great companies aren't built in obvious, crowded spaces. Cursor, like Stripe, Figma, and Shopify before it, started with a non-consensus belief about the future. Before ChatGPT, they believed AI would transform knowledge work. They focused on a genuinely exciting domain, became their own customer, and obsessed over power users. Their journey involved years of "glass-chewing" effort before the market was ready. The pattern is consistent: identify a long-term technological shift, find a missed entry point, and execute for years before the trend becomes obvious. First-generation products (PayPal, Adobe, Amazon) prove a market exists. Second-generation winners (Stripe, Figma, Shopify) rebuild that market around new insights, technology, or changing customer behaviors. Founders must identify their phase in the cycle. Early entrants like Coinbase or Cursor focus on making new technology usable for power users. Later entrants find the "yin" to the established "yang"—the blind spots incumbents miss as they grow distant from individual users. The key is deep market immersion. Use every product in your space. Talk to users. Build an audience. Stop looking for ideas and start *seeing* them everywhere. Then, choose one. The idea must offer a 10x improvement or solve a "hair-on-fire" pain point—something severe enough that users are already crafting workarounds. When building, avoid feature bloat. Ask: why would someone switch? Great startups rarely force new behaviors; they improve familiar workflows with drastically lower friction (e.g., Cursor forked VS Code instead of creating a new editor). Distribution is the underestimated moat. Before product-market fit, achieve distribution-market fit. How do customers discover new tools? Founders like those at Airbnb, Stripe, and Cursor did unscalable, manual work to recruit early users. The final, unteachable ingredient is resilience. Cursor built for years pre-market, faced rejection, and persisted. So did Airbnb, Nvidia, and Rain (which launched post-FTX collapse). The lesson isn't that these founders were smarter, but that they stayed in the game long enough for their insights to compound. Framework: Spot technological cycles. Cultivate unique insight. Obsess over your market. Talk to customers. Find a hair-on-fire problem. Build the simplest wedge. Win your distribution channel. Above all, don't quit when it gets hard. Most people won't do these things consistently. The few who do build the next generation of great companies. Go build.

marsbit2h ago

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

marsbit2h ago

Trading

Spot
Futures
活动图片