Coinbase rolls out stock trading, prediction markets in ‘everything app’ push

cointelegraphОпубліковано о 2025-12-17Востаннє оновлено о 2025-12-17

Анотація

Coinbase has expanded its services by launching stock trading and prediction markets as part of its strategy to become an "everything app." The crypto exchange announced the availability of stock and ETF trading in the U.S., with plans for 24/7 global access. Additionally, Coinbase partnered with Kalshi to introduce prediction markets, initially rolling out outside the U.S. The company also revealed plans to offer 24/7 perpetuals with high leverage early next year, allowing bets on both crypto and stocks. These moves align with Coinbase's broader rebranding effort earlier this year to include apps, social features, and chat functionalities.

Coinbase is launching stock trading and prediction markets as the crypto exchange looks beyond digital assets to chase its ambition of creating an “everything app.”

Coinbase head of consumer and business products, Max Branzburg, announced at the company’s year-end conference that “stock trading is now available on Coinbase.”

“This is a major milestone in our plan to enable 24/7 trading of stocks and ETFs from anywhere in the world, powered by crypto,” he said.

Branzburg also said that Coinbase has launched prediction markets in partnership with Kalshi, which will begin its rollout today and later come to the US.

Last month, tech researcher Jane Manchun Wong discovered that Coinbase was developing a prediction markets platform, which indicated it would be backed by Kalshi.

Related: Crypto’s ‘super app’ race is on as industry enters aggregation era: Report

Coinbase is also set to offer 24/7 perpetuals early next year, which will allow users to bet on crypto and stocks gaining or dropping with up to 50 times leverage.

Branzburg said that the new product lineup is part of Coinbase’s “everything exchange.” The company started to lay the groundwork for a wider set of offerings in July, rebranding its wallet app as an “everything app” that added apps, social networks and chat features.

Magazine: Can Robinhood or Kraken’s tokenized stocks ever be truly decentralized?

Пов'язані матеріали

a16z: Top Talent Flows to AI Infrastructure, Infrastructure Design Will Be 'Redesigned from Scratch'

a16z Unveils "Machine Age Fund": AI Infrastructure Faces Massive Overhaul Silicon Valley VC giant a16z (Andreessen Horowitz) has launched a new "Machine Age Fund" dedicated to AI infrastructure, citing a vast and growing "supply-demand fracture." Key takeaways: * **Unlimited Demand vs. Constrained Supply:** AI demand is growing exponentially (estimated near 1000% annually for tokens), while supply chains for chips, memory, data centers, and power are booked through 2027-2028. GPU prices are rising against historical trends. * **A Resource Problem, Not Engineering:** The bottleneck is no longer software engineering but physical resources (hardware, power, cooling). Money and compute directly translate to intelligence output, removing traditional scaling limits. * **Complete Infrastructure Rebuild Needed:** Existing data centers and computing stacks, designed for a different era, are hitting physical limits. Everything needs rethinking from first principles: chip architecture, memory hierarchy, networking, power delivery (shifting to 800V DC), and cooling (moving to liquid). * **Investor & Founder Shift:** Top entrepreneurs are increasingly moving into hardware, with deals in the space rising from ~3-5% to over 20-30% of a16z's top-tier deal flow. Founders need to be "systems thinkers" who understand manufacturing and supply chains. * **Massive Economic Scale:** Training a frontier model now costs $3-5B. With inference needing to recoup ~$10B, saving 20% in efficiency ($2B) can justify developing a custom ASIC for a single model—a previously unthinkable economic dynamic. * **Long-Term Horizon:** a16z believes we are in the very early stages of a decades-long era where compute is applied to vast new domains (science, materials, biology, creative work). The firm re-frames AI as "Machine Intelligence," emphasizing the critical, foundational role of hardware in this new age.

marsbit31 хв тому

a16z: Top Talent Flows to AI Infrastructure, Infrastructure Design Will Be 'Redesigned from Scratch'

marsbit31 хв тому

OpenAI Buys Tens of Thousands of Macs for Reinforcement Training! NVIDIA's Job Gets Snatched by Apple

OpenAI is reported to have purchased tens of thousands of Mac mini and Mac Studio computers for reinforcement learning tasks, specifically to train "computer-use agents." This surge in demand from AI companies like Anthropic as well has made Mac Apple's fastest-growing product line. Apple is promoting its hardware's suitability for local AI processing, highlighting advantages like the unified memory architecture of M-series chips and robust cooling in desktop Macs. However, this unexpected enterprise demand has led to supply shortages for high-end configurations. Nvidia has taken notice, launching its DGX Spark desktop AI computer as a direct competitor. While Apple has begun building internal servers with its chips for AI tasks, it currently does not sell them, relying instead on partners to expand its enterprise reach.

marsbit43 хв тому

OpenAI Buys Tens of Thousands of Macs for Reinforcement Training! NVIDIA's Job Gets Snatched by Apple

marsbit43 хв тому

ChatGPT's New Model Bel Rumored to Have Completed Pre-training with a Staggering 10 Trillion Parameters

ChatGPT's new model "Bel," reportedly pre-trained with a staggering 10 trillion parameters, signals OpenAI's aggressive push toward AGI. This "monster" model, succeeding the "Doug" base model and targeting the post-GPT-6 era, allegedly surpasses the upcoming Astra model in coding, reasoning, and long-horizon agent tasks. It can operate autonomously for days, self-recover, and coordinate hundreds of sub-agents. The report claims OpenAI has consolidated computing power, even pausing projects like Sora, to focus on these massive models. With its proprietary "Jalapeño" chips and significant computational advantage, OpenAI reportedly believes competitors like Anthropic cannot keep pace. Internally, OpenAI is said to be achieving recursive self-improvement (RSI), using AI to optimize its own systems and drastically cut costs. Future visions include ultra-fast, low-latency AI enabling real-time "flow state" interaction, a shift to cloud-based agent clusters over local compute, and the emergence of a "Personal AGI" that passively understands and proactively assists users. While the specifics of Bel remain unconfirmed, the leaks portray an AI race where scale and compute are key, with OpenAI positioning itself to maintain a dominant lead through 2026-2027.

marsbit52 хв тому

ChatGPT's New Model Bel Rumored to Have Completed Pre-training with a Staggering 10 Trillion Parameters

marsbit52 хв тому

Claude's Safety Mechanism Backfires Spectacularly, AI Deletes Developer's 700GB Home Directory in a Fit of Rage

Claude's safety mechanisms backfired spectacularly, leading an AI to delete a developer's entire 700GB home directory. The developer, Guillemot, asked Claude Fable 5 to write a script to create isolated sandbox folders in /tmp for AI agents and clean them up afterward, ensuring files in use wouldn't be deleted. Because the task involved risky delete operations, Claude's built-in safety system triggered an "adversarial review," automatically downgrading the model from the capable Fable 5 to the more conservative Opus 5, and then to Opus 4.8, to perform a security check. The Opus 4.8 model successfully tested the script, correctly identifying the /tmp and home directories as off-limits for deletion. However, during the cleanup phase that followed the test, it reused a variable name from the testing phase. This variable contained the path to the user's home directory. Consequently, the model executed a delete command on that exact path it had just deemed unsafe, wiping out 700GB of data. The intended /tmp directory remained untouched. The incident highlights critical flaws in Anthropic's safety downgrade system, which community developers have criticized for being overly sensitive, reducing model capability for complex tasks, and being "sticky" for an entire session once triggered. The irony is that a mechanism designed to hand off dangerous tasks to a safer, more conservative model instead delegated it to a weaker model that made a catastrophic error in variable scope and path handling.

marsbit53 хв тому

Claude's Safety Mechanism Backfires Spectacularly, AI Deletes Developer's 700GB Home Directory in a Fit of Rage

marsbit53 хв тому

Starting from Anthropic, Dissecting the Hyperliquid Perpetual Contract Sector

The article explores the emergence of pre-IPO perpetual synthetic asset markets on Hyperliquid's HIP-3 protocol, focusing on platforms like Ventuals, Trade.xyz, and Entropy. It begins with the intense secondary market demand for Anthropic stock, as illustrated by Jesse Leimgruber's experience. The HIP-3 protocol allows anyone to launch a perpetual DEX by locking $40M, with 30% of Hyperliquid's volume flowing through it. The piece details the rise and fall of Ventuals, the first major platform for trading pre-IPO synthetics like Anthropic and SpaceX. Its failure was due to extreme funding rates (reportedly hitting 8,700% annualized for Anthropic) and a pricing model vulnerable to thin liquidity, leading to a 45% crash in a SpaceX contract. Trade.xyz succeeded by using a simple 30-minute internal TWAP for pricing and dominates HIP-3 volume. Entropy, backed by a $14M Ribbit Capital-led round, attempts to improve on Ventuals by capping funding rates and using a hybrid oracle that blends its order book with private market valuations, while pricing Anthropic by total market cap. However, it struggles with accurately pricing private companies like Anthropic, unlike public stocks like SanDisk (SNDK) where arbitrage bots align prices. The article concludes by noting a potential Kraken testnet deployment on HIP-3, suggesting regulated entities may adopt its technology within permissioned frameworks. The evolution of these platforms highlights the challenges and iterative progress in creating decentralized markets for private company exposure.

marsbit1 год тому

Starting from Anthropic, Dissecting the Hyperliquid Perpetual Contract Sector

marsbit1 год тому

Торгівля

Спот
活动图片