Peter Schiff sparks tokenized Gold vs. Bitcoin debate: ‘We accept BTC’

ambcryptoPublicado a 2026-03-16Actualizado a 2026-03-16

Resumen

Peter Schiff, a prominent gold advocate and Bitcoin critic, has reignited the debate between the two assets by announcing his company, SchiffGold, will accept Bitcoin payments. This move is a strategic effort to promote his new tokenized gold product, which he claims offers a more stable and reliable store of value. Schiff's argument centers on gold's historical role as money and his belief that Bitcoin is a highly speculative asset. The announcement is seen as a direct challenge to Bitcoin proponents, framing the acceptance of BTC as a means to demonstrate the superiority of his gold-backed token.

Preguntas relacionadas

QWhat is the main topic of the debate sparked by Peter Schiff?

AThe debate centers on the comparison between tokenized gold and Bitcoin, specifically regarding which asset people are more willing to accept.

QAccording to the title, which asset does 'we' refer to in the statement 'We accept BTC'?

AThe title does not explicitly specify who 'we' refers to, but it is part of Peter Schiff's argument in the debate, suggesting a group or his perspective is now accepting Bitcoin, which is notable given his historical criticism of it.

QWhat is Peter Schiff's known historical stance on Bitcoin, making this debate significant?

APeter Schiff is a well-known gold advocate and a prominent cryptocurrency skeptic who has frequently criticized Bitcoin, so his statement about accepting BTC is a significant and surprising development that sparks debate.

QWhat does the term 'tokenized Gold' refer to in this context?

A'Tokenized gold' refers to digital tokens that are backed by and represent ownership of physical gold, allowing it to be traded on blockchain platforms.

QWhat visual element accompanies the article based on the provided HTML code?

AThe article is accompanied by an image with the source URL 'https://d1x7dwosqaosdj.cloudfront.net/images/2026-03/f15d98a84ae441cb9199b2a002082ebe.png', which is displayed as a post thumbnail.

Lecturas Relacionadas

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

A deep dive into Anthropic's internal methodology for building effective AI "Skills" reveals five key insights for maximizing their value. First, Skills should focus on capturing "Gotchas" and tacit organizational knowledge—like common pitfalls and undocumented rules—rather than restating general information the AI already knows. Second, think of Skills as a form of "Context Engineering"; they are best structured as folders, not monolithic documents. A core `SKILL.md` file should act as a navigational index, progressively pulling in detailed references, examples, and assets only as needed to avoid overwhelming the model's context window. Third, whenever possible, automate repetitive tasks with scripts. This preserves the model's reasoning capacity for judgment and analysis, while scripts reliably handle the execution, saving tokens and improving accuracy. Instructions within a Skill provide the "why" and the expert judgment, while scripts provide the concrete "how." Fourth, a Skill's description is critical and often misunderstood. It should not be a list of features but a routing rule that clearly signals *when* the Skill should be triggered based on user intent and common phrasing. Finally, as Skills scale from personal tools to team-wide assets, management is crucial. Anthropic advocates for a lightweight, organic approach: let new Skills spread organically within small groups first. Those that prove genuinely useful through adoption naturally graduate to a formal marketplace, ensuring the curated library contains only high-value, battle-tested tools.

marsbitHace 4 min(s)

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

marsbitHace 4 min(s)

Vying for the AI Payment Track: Traditional Card Networks Face Off Against Coinbase

As AI agents increasingly conduct commercial transactions, a battle for control over the underlying payment infrastructure is unfolding. The competition centers on two divergent and incompatible technical approaches for autonomous AI payments. One camp, led by traditional card networks Visa and Mastercard, relies on tokenized card credentials within the established banking rails. Visa's "Intelligent Commerce" and Mastercard's "Agent Pay" services extend their existing tokenization technology to authorized AI agents for consumer retail transactions, leveraging decades of fraud protection and dispute resolution systems. Their partners include major AI firms like Anthropic, OpenAI, and Microsoft. The opposing camp, spearheaded by Coinbase, advocates for an open internet protocol using stablecoins. Coinbase's x402 protocol utilizes the HTTP 402 status code to enable direct, machine-to-machine micropayments with USDC on-chain. This model eliminates card fees and is designed for high-frequency, low-value transactions between AI agents, such as paying for API calls or data streams, where traditional card costs are prohibitive. Currently, application scenarios are clearly divided. Mainstream consumer-facing AI shopping services (e.g., ChatGPT's "one-click checkout," Amazon's AI-assisted shopping) predominantly use card channels due to their mature consumer protections and merchant networks. Conversely, the stablecoin channel dominates machine-to-machine payments, as seen in Amazon Bedrock's core payment service using Base blockchain. Significantly, traditional card networks are not solely defending their turf; they are also investing in the stablecoin arena. Visa has rapidly expanded its stablecoin settlement volume and partnered with Coinbase on interoperability, while Mastercard moved to acquire stablecoin platform BVNK. This dual-strategy indicates their intent to become the fee-collecting gateway for all payment flows, regardless of the underlying rail. The short-term outlook is for coexistence: cards for personal retail, stablecoins for machine transactions. The long-term outcome hinges on whether AI-driven commerce will resemble traditional retail or evolve into a vast network of machine micropayments. Visa and Mastercard's hedging strategy suggests they are prepared for either future, while companies betting on a single channel face greater risk.

Foresight NewsHace 6 min(s)

Vying for the AI Payment Track: Traditional Card Networks Face Off Against Coinbase

Foresight NewsHace 6 min(s)

For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

For the first time, a purely human-video-pretrained Vision-Language-Action (VLA) model for dexterous manipulation requires only a small amount of data for fine-tuning to achieve successful real-world deployment. Achieving human-level dexterous manipulation remains a core challenge in robotics. While multi-fingered hands offer hardware potential, Visual-Language-Action (VLA) models lag behind due to the high cost of collecting diverse, high-quality robot data. A novel framework, VITRA, developed by Microsoft Research Asia and Tsinghua University, addresses this by automatically transforming massive, unlabeled real-world human activity videos into a structured V-L-A training dataset. Key innovations include precise 3D hand motion annotation from monocular video, atomic action segmentation based on hand-speed minima, and automated instruction generation using VLMs combined with 3D trajectory visualization. This process created a massive dataset of 1 million clips. Pretrained exclusively on this human video data, the VLA model (combining a VLM backbone with a Diffusion Transformer action expert) demonstrates strong zero-shot hand motion prediction in unseen environments. Crucially, it requires minimal fine-tuning (~1.2k demonstrations) on real robot data to achieve high-success-rate dexterous manipulation tasks like grasping, placing, pouring, and sweeping on hardware like the Realman robot with the XHAND1 dexterous hand. The model shows exceptional generalization to novel objects and environments. The research also observes promising scaling behavior, where performance improves with more pretraining data, paving the way for more generalized embodied intelligence.

marsbitHace 16 min(s)

For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

marsbitHace 16 min(s)

Ray Dalio: The Revival of a China-led 'Tribute System', and the AI Industry Will Develop Like the EV Industry

Ray Dalio, founder of Bridgewater Associates, discusses shifting global power dynamics and the AI competition between the U.S. and China. He argues U.S. credibility as a global power willing to fight for its interests is declining, while China is accumulating wealth and influence. Dalio observes a trend where world leaders visit China, which he compares to a modern "tribute system"—not oppressive, but a hierarchy where nations acknowledge China's strength, affecting trade and security. This shift creates market uncertainty, prompting investors to seek asset diversification, including gold. Regarding AI, Dalio contrasts the U.S. and Chinese approaches. U.S. companies like OpenAI focus on monetization and subscriptions for revenue. In contrast, China treats AI as a public utility, akin to electricity or water, aiming for widespread worker accessibility over immediate profitability. He draws a parallel to China's electric vehicle industry success, where companies like BYD achieved rapid market growth. Dalio and JPMorgan's Mary Callahan Erdoes note China lacks U.S.-style fear of AI displacing jobs; instead, it focuses on leveraging AI for development and dominance in next-generation industries like robotics.

Odaily星球日报Hace 43 min(s)

Ray Dalio: The Revival of a China-led 'Tribute System', and the AI Industry Will Develop Like the EV Industry

Odaily星球日报Hace 43 min(s)

Michael Saylor's Latest Thoughts: The Four Beliefs That Determine Bitcoin's Future

Michael Saylor outlines four key ideologies shaping Bitcoin's future: Bitcoin Maximalists view it as the dominant monetary network and a breakthrough in economic empowerment. Bitcoin Capitalists focus on integrating Bitcoin into the global economy—into capital markets, banking, and corporate strategies—to realize its full potential as digital capital. Bitcoin Technologists advocate for ongoing protocol improvements in scalability, privacy, and security to ensure Bitcoin evolves with technological demands and threats. Bitcoin Fundamentalists prioritize protecting Bitcoin's core principles of self-custody, decentralization, and its use as sound money, guarding against dilution or capture. Each group addresses a vital aspect: Maximalists provide conviction, Capitalists drive adoption, Technologists tackle technical challenges, and Fundamentalists defend the protocol's integrity. The article argues that Bitcoin's path forward requires a balanced synthesis of these perspectives—maintaining a secure, immutable core while enabling disciplined innovation and integration in higher layers. The ultimate challenge is for Bitcoin to remain true to its principles while becoming useful to the broader world.

marsbitHace 57 min(s)

Michael Saylor's Latest Thoughts: The Four Beliefs That Determine Bitcoin's Future

marsbitHace 57 min(s)

Trading

Spot

Futuros