X is moving toward integrating financial services more deeply into its social platform with a feature known as Smart Cashtags. Nikita Bier, the platform’s head of product, signaled in a weekend post that users will soon be able to trade stocks and crypto directly from their timeline, marking a significant step in bringing traditional markets and digital assets closer to social interaction. The timeline for rollout, Bier indicated, includes a sequence of features launching in the next few weeks, suggesting a staged approach rather than a single, sweeping update. The idea builds on X’s previous experiments with Cashtags and reflects broader ambitions to weave commerce and finance into everyday activity on the app, potentially altering how millions interact with markets while they scroll.

The concept isn’t entirely new for X. In 2022 the platform introduced a basic Cashtag system designed to track the prices of major stocks and cryptocurrencies and to present visual financial data for assets such as Bitcoin (CRYPTO: BTC) and Ether (CRYPTO: ETH). That pilot was later discontinued, but the renewed emphasis on in-app trading signals a more ambitious plan to keep price data, discussion, and execution under one roof. The current push appears to be part of a broader strategy to reclaim a central role for financial functionality within the app, rather than relegating it to standalone apps or separate tabs. The public focus on Smart Cashtags and in-timeline trading follows a January teaser that hinted at in-app trading, though the company did not formally confirm the rollout at that time.

Cointelegraph reached out to X for comment about the Smart Cashtags feature and the timing of its launch, but the company did not respond by publication. The moves come as X, under the leadership of Elon Musk, has repeatedly emphasized a vision of the platform evolving into an “everything app”—a space where social interaction, payments, and transactions are seamlessly integrated. Musk has framed this ambition in terms of replacing multiple standalone apps with a single, feature-rich experience, drawing comparisons to WeChat, the Chinese messaging and payments ecosystem that blends social, financial, and commerce functions in one place.

While attention centers on Smart Cashtags, X is advancing another major line of development: X Money. Musk touched on the payments initiative during a recent All Hands presentation hosted by his AI venture, xAI. He outlined that X Money is still in a limited beta phase for roughly two months before any worldwide rollout. The aim, he said, is for X Money to become “the place where all money is” and a primary hub for monetary transactions on the platform. Musk’s comments underscore a strategic push to embed payments so deeply into daily use that the app becomes a de facto financial operating system for its hundreds of millions of users. In his remarks, Musk also emphasized the platform’s substantial reach, noting that X serves roughly 600 million average monthly users and articulating a vision in which a user could conceivably conduct most, if not all, daily digital activities within the app.

The broader context for these developments is the evolving landscape of social platforms that fuse content with financial services. If successful, Smart Cashtags could provide a frictionless entry point for casual users to engage with markets, while more seasoned traders may appreciate the convenience of real-time price feeds, charts, and trading execution within a single interface. The rollout also signals X’s continued experimentation with monetizable features that extend beyond advertising or content curation, shifting the platform toward a more transactional, payments-enabled model. The integration of trading and payments may also influence how brands, creators, and communities monetize engagement as users gain easier access to financial tooling alongside social interaction.

X inches into payments as it attempts to become an “everything app”

Elon Musk provided an update on the launch timeline for X Money, the platform’s payments feature that would enable users to send money to one another, in another high-profile public forum. Speaking at a recent All Hands event run by xAI, Musk indicated that X Money remains in limited beta testing for the next two months, after which a global rollout would follow. The goal is not merely to add another payments tool; it is to establish X Money as the central backbone for all monetary activity on the platform, a foundational capability that could influence how users view and rely on the app for everyday financial tasks. Musk’s framing of X Money as a unifying payment layer reinforces the broader strategy to turn X into an integrated ecosystem where social activity and financial transactions coexist seamlessly.

With roughly 600 million average monthly users, Musk suggested that the potential for such an integration is vast. The assertion underscores the strategic importance of any feature that can convert passive engagement into routine financial interactions. The concept of “everything in one place” has long animated the ambitions of tech leaders who seek to reduce the friction between social media and commerce. If the beta phase demonstrates reliability and security at scale, a worldwide rollout could significantly accelerate how users conduct payments and financial exchanges within the app, potentially affecting user behavior, merchant adoption, and the overall demand for linked financial services on social platforms.

Why it matters

The Smart Cashtags initiative, if realized, could redefine the user journey from curiosity to action on social media. Rather than leaving the app to check prices, follow tickers, or execute trades on separate platforms, users might access real-time information and make transactions in a single, familiar environment. This could lower the perceived barriers to entry for crypto investors and stock traders who are comfortable with the social context, potentially boosting liquidity and engagement around a wider range of assets. The integration would also place pressure on competing social networks and fintechs to offer similar in-app capabilities, raising the bar for how social apps monetize and retain users through integrated financial services.

From a regulatory and risk-management standpoint, the move to in-app trading and payments raises important considerations. In-app trading raises questions about suitability, know-your-customer (KYC) standards, and consumer protection within social platforms. The beta and phased rollout approach may help X observe user behavior, test security controls, and refine compliance workflows before reaching a broad audience. While the exact list of supported assets remains to be clarified, the emphasis on Bitcoin (CRYPTO: BTC) and Ether (CRYPTO: ETH)—the two largest crypto assets by market capitalization—signals that the feature aims to cover both traditional and digital assets in parallel.

What to watch next

• Official confirmation and timing for the Smart Cashtags rollout, including which assets will be tradable at launch.
• Details on the X Money beta scope, duration, and security enhancements prior to a global rollout.
• Regulatory updates or disclosures from X regarding compliance, user protections, and suitability testing for in-app trading.
• Any public statements from X or xAI about partnerships, supported brokers or custody arrangements, and asset coverage beyond BTC and ETH.

Sources & verification

• Nikita Bier’s public post on X regarding Smart Cashtags and a timeline for the rollout.
• The January teaser image signaling a potential in-app trading rollout for Smart Cashtags.
• Cointelegraph reporting on X’s Smart Cashtags development and the 2022 Cashtag system, including reference to BTC and ETH data.
• Elon Musk’s All Hands presentation at xAI discussing X Money and its beta status and rollout plans.
• Official statements and posts from X and xAI related to payments and financial features in development.

Smart Cashtags and in-app trading: what the plan could change for X users

X’s renewed focus on in-app trading via Smart Cashtags represents a meaningful pivot from a primarily social platform to a hybrid financial product. The first wave of changes centers on enabling trades directly from the timeline, a move designed to reduce friction for users who want to act on information they encounter in their feed. The initiative aligns with a broader push to consolidate services within a single app, an approach Musk has long described as a path toward creating an “everything app.” The potential impact on user habits could be substantial: a user who previously opened a separate brokerage or crypto exchange app might instead transition to executing trades while scrolling through posts, creating new kinds of engagement loops and monetizable moments for the platform.

From a user experience perspective, Smart Cashtags would need to balance speed with security and clarity. Real-time price data, simple execution flows, reliable order types, and clear disclosures about risk are essential for adoption. The 2022 Cashtag experiment established a framework for asset price visualization but did not culminate in a full trading experience. The new approach would require robust risk controls, transparent fee structures (if any), and intuitive interfaces that resonate with both casual observers and more active traders. The public discourse around in-app trading on social platforms has grown louder in recent years, and X’s iteration could influence how other apps approach embedding financial functionalities in social feeds.

The broader context also includes X Money, a payments feature Musk described as aiming to centralize monetary transactions within the app. The beta approach—limited for two months before a worldwide rollout—suggests a cautious, iterative path toward the final product. If successful, X Money could reduce the need for switching between apps when sending money, splitting bills, or conducting peer-to-peer transfers. This seamless integration of payments and trading could change demand patterns for both fiat-based and crypto-based transactions, potentially increasing on-platform liquidity and widening the audience for financial services embedded in social experiences. The lines between social engagement, information gathering, and financial activity could blur further as features like Smart Cashtags and X Money mature.

In the longer run, the vision of an “everything app” rests on user trust, reliability, and a coherent experience. The path will likely involve ongoing refinement of the user interface, more granular control over asset types, and stronger safeguards to protect users from mispricing, scams, or missteps in fast-moving markets. As X navigates these challenges, observers will be watching closely for how the platform handles compliance, data privacy, and cross-border considerations—issues that have become central to the broader dialogue around fintech on social platforms. Whether the Smart Cashtags initiative becomes a core daily utility or remains a niche feature will depend on how convincingly the platform demonstrates value, safety, and ease of use to a global audience.

TLDR:

• China’s U.S. Treasury holdings fell to $682.6B, down from a $1.3T peak in 2013.
• China’s share of foreign Treasury holdings dropped to 7.3%, the lowest since 2001.
• Gold reserves reached 2,308 tonnes after 15 straight months of central bank buying.
• Total foreign U.S. Treasury holdings hit a record $9.36T despite China’s reduction.

China’s holdings of U.S. Treasuries declined to their lowest share in foreign reserves since 2001. As of November 2025, Beijing held $682.6 billion in U.S. government debt, while its gold reserves climbed to record levels.

Treasury Holdings Decline to Multi-Decade Low Share

Data shows China’s Treasury holdings dropped to $682.6 billion in November 2025. This marks a sharp fall from its 2013 peak of over $1.3 trillion.

China now accounts for 7.3% of total foreign-held U.S. Treasuries. That share is the lowest recorded since 2001. Despite the decline, overall foreign holdings reached a record $9.36 trillion. Japan and the United Kingdom remain the largest foreign holders.

The reduction has drawn attention across financial markets. However, bond markets have remained stable during the adjustment period. The figures indicate a gradual rebalancing rather than an abrupt market disruption.

On X, user Wimar X claimed that China “dumped $638 billion” in U.S. Treasuries. The post also stated that current holdings are the lowest since 2008. The tweet further suggested China is “exiting the system.”

Official data confirms the decline in holdings. However, total foreign demand for Treasuries remains strong, led by other major economies.

Gold Reserves Rise for 15 Consecutive Months

At the same time, the People’s Bank of China continued adding gold to its reserves. January 2026 marked the fifteenth straight month of gold purchases.

China’s gold holdings reached 2,308 tonnes, valued at about $370 billion. Gold now represents roughly 5% of the country’s $3.3 trillion in total reserves. This is the highest recorded level for China’s gold stockpile.

Some market observers view the shift as a move toward hard assets. Others describe it as standard reserve diversification. The increase in gold has occurred alongside the steady reduction in Treasury exposure.

Even so, China remains one of the largest holders of U.S. government debt globally. The adjustment appears gradual rather than sudden.

The combination of lower Treasury holdings and higher gold reserves reflects changing reserve allocations. Meanwhile, global Treasury markets continue to operate without major volatility.

Ethereum co-founder Vitalik Buterin has grown wary of how prediction markets are evolving, warning they risk becoming short-term price betting engines rather than tools that support long-term infrastructure. In a post on X, he argued that the current trajectory shows “over-converging” focus on immediate price moves and speculative behavior. He called for a shift toward onchain prediction markets that serve as hedges against price exposure for consumers, rather than betting mechanisms that amplify fiat-driven volatility. The thrust of his critique centers on moving from pure price bets to broader markets that can stabilize expenditures over time. He suggested a framework that blends prediction markets with AI-driven tools to counter inflationary pressures faced by households and businesses alike. In essence, his stance positions prediction markets as potential risk-management primitives if redesigned with real-world spending in mind.

Key takeaways

• Buterin argues prediction markets are tilting toward short-horizon price betting, which he views as unhealthy for long-term building in crypto and beyond.
• He envisions a model where onchain prediction markets are paired with AI large-language models to hedge consumer price exposure across goods and services.
• The proposed system would create price indices by major spending categories and regional differences, with prediction markets for each category.
• Each user could have a local LLM that maps their expenses and generates a personalized basket of prediction-market shares representing several days of future outlays.
• Supporters say such markets can offer valuable market intelligence and hedging capabilities, potentially improving price stability in a fiat-dominated environment.
• Existing prediction-market platforms like Polymarket and Kalshi are cited as part of the broader ecosystem that could be reoriented toward hedging and risk management rather than speculative bets.

Tickers mentioned: $ETH

Sentiment: Neutral

Market context: The discussion sits at the intersection of onchain finance, risk management, and regulatory scrutiny, as investors and developers weigh how to apply AI tools to price hedging while navigating evolving policy debates around prediction markets.

Why it matters

The idea of coupling onchain prediction markets with AI-assisted personal finance tools signals a broader attempt to retrofit crypto-native mechanisms for real-world stability. If successful, the approach could reframe how individuals and businesses manage price risk—shifting from a speculative posture to a practical hedging framework that protects purchasing power in an inflationary fiat environment. Buterin’s proposal emphasizes a user-centric model in which private data about expenses informs a custom set of market instruments. That alignment between individual spending patterns and market-based hedges could, in theory, yield more predictable budgeting for everyday goods and services.

Critics of prediction markets often point to concerns about manipulation, liquidity distribution, and regulatory risk. But proponents argue that when linked to digital, onchain ledgers and AI-driven personalization, these markets can deliver more resilient price signals and a public-good function by aggregating diverse information. The debate touches broader questions about how decentralized finance should interact with traditional market dynamics and consumer protection standards. In this framing, the role of prediction markets extends beyond forecasting political events or commodity prices to becoming a probabilistic toolkit for household and business planning.

As the ecosystem evolves, the boundary between information services and financial instruments remains a focal point for policymakers and practitioners alike. The discussion around onchain prediction markets is part of a wider push to explore how AI can augment human decision-making in finance, risk assessment, and purchasing power. The outcome will hinge on how convincingly the model demonstrates real-world utility, addresses liquidity and governance challenges, and remains compliant with applicable rules in various jurisdictions.

What to watch next

• The publication of any whitepapers or technical notes detailing the proposed onchain prediction-market architecture and the role of local LLMs in personal expense modeling.
• Emerging experiments or pilot programs that test category-based price indices and category-specific prediction markets in real-world settings.
• Regulatory responses or clarifications around prediction markets and onchain hedging tools, particularly in jurisdictions weighing consumer protection and market integrity.
• Public discussions and research from academics and practitioners about the feasibility and governance of personalized prediction-market portfolios.
• Follow-up statements or interviews from Vitalik Buterin or affiliated teams that expand or refine the proposed framework.

Sources & verification

• Vitalik Buterin’s X post outlining concerns about prediction markets and the proposed shift to hedging mechanisms. Link: https://x.com/VitalikButerin/status/2022669570788487542
• Cointelegraph op-ed discussing onchain prediction markets and the integration of AI LLMs. Link: https://cointelegraph.com/opinion/blockchain-prediction-markets
• Cointelegraph coverage on prediction markets and information markets, including perspectives on market intelligence. Link: https://cointelegraph.com/news/prediction-markets-information-finance
• Cointelegraph coverage of academic perspectives on prediction markets, including comments from Harry Crane of Rutgers University. Link: https://cointelegraph.com/news/prediction-markets-polymarket-polls
• CFTC-related developments regarding proposals on prediction markets, cited in Cointelegraph coverage. Link: https://cointelegraph.com/news/cftc-withdraws-proposal-ban-sports-political-prediction-markets

Rethinking prediction markets as hedging tools with AI

Ethereum co-founder Vitalik Buterin has grown wary of how prediction markets are developing, warning they risk becoming short-term price betting engines rather than tools that support long-term infrastructure. In a post on X, he argued that the current trajectory shows “over-converging” focus on immediate price moves and speculative behavior. He called for a shift toward onchain prediction markets that serve as hedges against price exposure for consumers, rather than betting mechanisms that amplify fiat-driven volatility. The thrust of his critique centers on moving from pure price bets to broader markets that can stabilize expenditures over time. He suggested a framework that blends prediction markets with AI-driven tools to counter inflationary pressures faced by households and businesses alike. In essence, his stance positions prediction markets as potential risk-management primitives if redesigned with real-world spending in mind. He proposed a system in which price indices are constructed across major spending categories, with regional variations treated as distinct categories, and a dedicated prediction market for each.

Buterin elaborates a mechanism where each user—whether an individual or a business—operates a local AI model that understands that user’s expenses. This AI would curate a personalized basket of market shares, effectively representing “N” days of predicted future outlays. The intent is to offer a dynamic hedge against rising costs, allowing participants to hold a mix of assets to grow wealth while maintaining a safety net against inflation via tailored prediction-market positions.

Supporters of prediction markets argue they provide valuable information about global events and financial trajectories, potentially serving as a hedge against a variety of risks. They point to platforms such as Polymarket and Kalshi as examples of how publicly sourced probabilities can supplement traditional data sources. Academic voices, including Rutgers professor Harry Crane, contend that well-structured prediction markets can outpace conventional polls in forecasting accuracy and should be treated as a public good in principle, assuming robust governance and safeguards. Critics, however, worry about misuse, regulatory constraints, and the potential for manipulation if markets are driven by centralized or biased actors. The debate straddles both the philosophy of information markets and the practical design challenges of turning them into reliable hedges for everyday life.

Ultimately, the question is whether a hybrid system—combining onchain markets with AI personalization—can deliver tangible price stability without sacrificing liquidity or inviting abuse. If such a model proves viable, it could redefine how crypto-native financial instruments interact with the real economy, offering tools that help households and firms weather price fluctuations while contributing to a broader ecosystem that values data-driven risk management.

TLDR:

• Lightning Labs released open-source toolkit enabling AI agents to transact with bitcoin independently. 
• The L402 protocol allows AI systems to pay for services without requiring accounts or authentication. 
• Remote signer architecture separates private keys from agent operations to prevent security breaches. 
• Agents can now purchase data feeds and sell services autonomously using bitcoin through micropayments.
  

Lightning Labs has released an open-source toolkit that enables artificial intelligence agents to send and receive bitcoin payments independently through the Lightning Network.

The technology eliminates the need for human intervention, traditional accounts, or API authentication systems. This development represents a major advance toward autonomous machine commerce, where AI systems can directly purchase data, services, and computational resources without human oversight.

Automated Payment Infrastructure for AI Systems

The new toolkit addresses a critical limitation in current AI agent capabilities. While modern AI systems can write code, analyze information, and execute complex tasks, they cannot easily conduct financial transactions.

Traditional payment methods require human identity verification through credit cards, bank accounts, and regulated payment platforms.

These systems depend on personal documentation and manual approval processes that AI agents cannot navigate.

Lightning Labs explained that agents face a fundamental barrier despite their technical sophistication. The company stated that AI systems still struggle with payments despite being able to read documentation and call APIs effectively.

This gap exists because agents need to transact instantly and programmatically at massive scale, requirements incompatible with conventional financial infrastructure.

The solution centers on L402, a protocol built upon the HTTP 402 “Payment Required” status code. When an AI agent attempts to access paid content or services, the server responds with a Lightning invoice.

The agent pays this invoice and receives cryptographic proof of payment. This proof functions as an access credential, allowing the agent to retrieve the requested resource.

Lightning Labs introduced “lnget,” a command-line tool that automates the entire payment process. When an agent encounters paid content, lnget handles invoice payment in the background without requiring manual steps.

The tool supports multiple Lightning backend configurations, including direct connections to local nodes and encrypted tunnel access through Lightning Node Connect.

Michael Levin, head of product development at Lightning Labs, emphasized the toolkit allows agents to use bitcoin payments without mandatory identification or registration requirements.

Security Architecture and Commercial Applications

Security measures form a core component of the toolkit’s design. The recommended configuration uses a remote signer architecture that separates private key storage from payment operations.

The signing machine holds private keys offline while the agent machine executes transactions. This separation ensures that compromised agent systems cannot expose private keys.

The macaroon-based credential system enables fine-grained permission control. Developers can create credentials limited to specific functions such as payment-only or read-only access.

These bearer tokens can be further restricted without issuing new credentials. The system supports five preset security roles tailored to different agent functions.

On the server side, Aperture enables developers to convert standard APIs into pay-per-use services. This reverse proxy handles L402 protocol negotiation and supports dynamic pricing based on resource consumption.

Backend systems require no Lightning-specific modifications. The combination creates a complete commerce loop where one agent can host paid services while another consumes them.

The toolkit enables direct agent-to-agent transactions at scale. AI systems can now purchase premium data feeds, acquire computational resources, and sell services for bitcoin.

This infrastructure supports micropayments that would be economically unfeasible with traditional payment rails. Lightning Labs positions the technology as foundational infrastructure for an emerging machine economy where autonomous agents conduct billions of programmatic transactions.

Ethereum co-founder Vitalik Buterin said he is starting to “worry” about the direction of prediction markets and suggested that they shift to become marketplaces to hedge against price exposure risk for consumers.

Prediction markets are “over-converging” to “unhealthy” products that are focused on short-term price betting and speculative behavior as opposed to long-term building, Buterin said in an X post.

Instead, onchain prediction markets coupled with AI large-language models (LLMs) should become general hedging mechanisms to provide consumers with price stability for goods and services, Buterin said. He explained how this system would work:

“You have price indices on all major categories of goods and services that people buy, treating physical goods and services in different regions as different categories, and prediction markets on each category. 

Each user, individual or business, has a local LLM that understands that user’s expenses and offers the user a personalized basket of prediction market shares, representing ‘N’ days of that user’s expected future expenses,” he continued.

Individuals and businesses can hold a combination of assets to grow wealth and “personalized prediction market shares” to offset the rising cost of living created by fiat currency inflation, Buterin concluded.

Related: CFTC pulls Biden-era proposal to ban sports, political prediction markets

Prediction markets are useful market intelligence tools, supporters say

Prediction markets are crowdsourced intelligence platforms that can provide insight into global events and financial markets, while allowing individuals and businesses to hedge against a wide variety of risks, proponents of prediction markets say.

Prediction markets are more accurate than polls and should be treated as a public good, according to Harry Crane, a statistics professor at Rutgers University.