TheDinarian
News • Business • Investing & Finance
Standards and interoperability: The future of the global financial system
April 14, 2024
post photo preview

TABLE OF CONTENTS

  • Introduction
  • The call for standards
  • Defining standards
  • A comprehensive overview of current standards on digital assets
  • Lessons learned from standard-setting efforts
  • Establishing standards
  • Key themes for a CBDC framework
  • Conclusion

Abstract

Over the past few years, the global financial landscape has undergone a significant transformation marked by the emergence and integration of digital assets. Looking ahead, the global financial terrain is set to include a spectrum of both sovereign and nonsovereign digital currencies and both centralized and decentralized networks. This future brings the promise of enhanced efficiency, inclusion, transparency, and choice to global payments. To fulfill this promise, the international community must develop interoperability standards that prioritize a fast, highly scalable, and resilient architecture. The flexibility of this architecture to adapt configurability based on policy and economic considerations is critical to its success.

This working paper is a foundational step toward a broader, global dialogue about digital asset standards. The Digital Dollar Project and the Atlantic Council’s GeoEconomics Center hosted a global convening titled “Exploring Central Bank Digital Currency: Evaluating Challenges and Developing International Standards” in November 2023. A version of this paper was released as a working paper to level set the attendees of the conference and provide a call to engage the public and the private sector in standard-setting efforts. This paper was further developed based on feedback from the conference and outreach afterward. The  paper now reflects what we learned from our convening and incorporates the most recent developments in standard-setting efforts globally. The rapid growth of central bank digital currencies (CBDCs) worldwide underscores the importance of aligning approaches to their development, adoption, and implementation across technical, regulatory, and governance levels. Today, there is a patchwork of first steps undertaken by both public-and private-sector entities, aimed at achieving different objectives. These efforts have focused on frameworks, guiding principles, and, in some cases, the development of standards for digital assets broadly, as described below. Some are CBDC-specific and others have general applicability in the payments sector. As governments and stakeholders collaborate to establish consistent benchmarks for CBDC development, it’s crucial to identify, organize, and align standard-setting endeavors. This process involves assessing existing efforts to pinpoint gaps and create a foundation for international standards that remain open and flexible for future development and innovation. Through this paper, we show the crucial element of interoperability, which is needed for the furtherance of standards on CBDCs and digital assets. We attempt to build the pressing themes around which standards will have to be addressed through existing and new efforts.

Introduction

In recent years, the global financial landscape has witnessed a profound transformation characterized by the accelerated rise and integration of digital assets. As a subset of these assets, central bank digital currencies (CBDCs) have captivated the interest of countries worldwide.1 The CBDC landscape has rapidly evolved with 130 countries, representing 98 percent of the global economy, actively researching and, in some cases, deploying CBDCs. A recent survey by the Bank for International Settlements (BIS) revealed that the number of central banks likely to issue a CBDC within the next three years has grown in the past year from 15 percent to 18 percent for retail CBDCs (rCBDC) and from 8 percent to 15 percent for wholesale CBDCs (wCBDC).2

CBDCs, in their promise and potential, are emblematic of a broader shift—a movement toward a more efficient, frictionless digital infrastructure, shaping the future of international trade, cross-border payments, and global financial integration. However, with transformative potential comes inherent complexity. As fiat currencies become more intertwined with technology there are significant implications for privacy, human rights, cybersecurity, digital financial inclusion, and the movement of money across borders for international trade, aid, investment, and other payments. If designed without a common framework of standards and collaboration, a shortsighted and fragmented approach to CBDC development could lead to the emergence of walled gardens.

At the core of establishing standards lies the concept of interoperability—the ability for diverse systems to interact seamlessly and reduce friction. In this context, interoperability extends beyond technical objectives alone; it requires a broader framework including regulatory and governance standards, paving the way for streamlined cross-border transactions, reduced operational friction, and bolstered trust among participating entities. While not a panacea, technical, regulatory, and governance benchmarks are instrumental in navigating the complexities of the international payments systems. In order to achieve interoperability, CBDC exploration should prioritize a thorough discussion on establishing technical, regulatory, and governance standards. (See Annex 1 for definitions relevant to this discussion.)

This paper is intended as a catalyst to stimulate a broader, global dialogue about CBDC standards. It takes stock of existing activities, begins to define how these efforts may be coordinated and aggregated into a set of globally accepted best practices, and offers a baseline for addressing gaps or deficiencies in defining best practices.

The call for standards

CBDCs are a digital form of a country’s national currency, issued and backed by the country’s central bank. They come in two forms: retail CBDCs (rCBDC), accessible to individual consumers and usable for everyday purchases and peer-to-peer payments, and wholesale CBDCs (wCBDC), utilized by financial institutions or other major entities for interbank settlements and large financial transactions. The motivations behind rCBDCs and wCBDCs are distinct. The deployment of rCBDCs is usually motivated by financial inclusion, payment efficiency, privacy, and safety. Interest in wCBDCs is aimed at addressing cross-border friction to improve international payments—including limited operating hours, long transaction chains, restrictions on legacy technology platforms, data fragmentation, high costs, complex funding, and compliance issues.3

Ultimately, rCBDCs and wCBDCs would operate in conjunction with each other to achieve both the domestic and cross-border needs of a country.4 Therefore, the deployment of domestic CBDCs must not be considered in isolation or the result will be walled gardens that stand apart from global commerce and economic trends. Creating a CBDC in a silo is unlikely to achieve the desired outcomes in the short or long term, as it will replicate the friction of the existing payments systems. CBDCs’ potential to provide a simpler and more efficient way to move money can only be realized as long as the CBDCs can interoperate with one another.

If deployed, a CBDC must be able to operate across various transactions, institutions, and users. Many CBDC initiatives and explorations recognize the complex and interconnected ecosystem in which financial activity takes place and the interdependencies of the different participants in transaction settlements. By agreeing on standards upfront—which is by no means a simple task—CBDCs can hopefully escape some of the growing pains that we have seen with the development of new financial technology (such as automated teller machines that could only be used by customers of a specific bank) or new digital technology (such as the challenges posed by the early years of closed-loop email).

Concentrating on developing and implementing clear and accessible global standards can enable greater industry collaboration and competitiveness through interoperability, transferability, consistency, and safety across various industries and economies. With this clarity, countries can direct their efforts toward aligning and promoting key principles such as privacy, free enterprise, the rule of law, and economic liberty within the global financial landscape.5

Defining standards

At the heart of this paper is the effort to promote interoperability in payments systems and prevent the creation of walled gardens. We therefore define standards as the technical, regulatory, and governance benchmarks needed to achieve interoperable systems in the long run. It is crucial to recognize that standards do not emerge arbitrarily; instead, they evolve from fundamental principles, embodying intentional consideration and consensus.

Standards specific to CBDCs are not unchanging; they must reflect and be responsive to technological development, market shifts, and experience. Standards are established by technical and governance bodies, often made up of diverse stakeholders, and reflect a consistent floor for pragmatic implementation across jurisdictions. Therefore, they must have built-in flexibility to adjust to changing circumstances across a variety of market structures.

Our use of a narrow definition of standards as a means to achieve interoperable payments systems helps navigate the complex technical, governance, and regulatory environment. In the following section, we catalog existing standards for digital assets and the institutions responsible for setting them.

A comprehensive overview of current standards on digital assets 

 

Methodology

 

Due to their rapid growth, global standard-setting bodies have had to regulate and harmonize the adoption and use of digital assets across borders. In this section we provide an overview of the prominent organizations that play a pivotal role in shaping the digital asset landscape. Understanding the functions, roles, and importance of these bodies is critical for fostering a safe, competitive, and inclusive digital economy. We explore global governance institutions—the International Monetary Fund (IMF) and Bank for International Settlements—as well as regulatory standard setters—the Basel Committee on Banking Supervision (BCBS), the Financial Action Task Force (FATF), the International Organization of Securities Commissions (IOSCO), the Committee on Payments and Market Infrastructures (CPMI), and the Financial Stability Board (FSB)—and technical bodies like the International Organization for Standardization (ISO). Since rCBDC projects have largely been in the pilot, development, and research stage while wCBDC projects are currently limited, standard-setting efforts in some bodies have focused on broader digital asset developments.

 

International Monetary Fund

As a key institution in international monetary cooperation and exchange rate stability, the IMF is instrumental in assisting its 190 member countries in managing economic change. Its expertise in macrofinancial surveillance can help identify vulnerabilities associated with digital assets and it can offer policy advice to enhance the resilience of economies.

In November 2023, the IMF released a virtual handbook on CBDCs, designed as a comprehensive guide for policymakers and experts in central banks and finance ministries. The plan for this evolving handbook is to offer about twenty chapters by 2026, with periodic updates to reflect the latest findings and viewpoints.6 The initial chapters address key topics like the framework for exploring CBDCs, product development, impacts on monetary policy, capital flow management, and financial inclusion.

A publication called, "IMF Approach to Central Bank Digital Currency Capacity Development", released in April 2023, outlines the IMF’s efforts to facilitate peer learning and develop analytical underpinnings for advising member countries on CBDCs. In addition to research, the IMF provides technical assistance, including the XC platform initiative.7 The XC platform, proposes a global centralized ledger to simplify and streamline cross-border payments. This initiative builds on the concept of wholesale CBDCs, but the platform includes commercial banks, payment providers, and central banks within a single, streamlined system. The XC model aims to reduce transaction costs and settlement times, making it an attractive option for countries looking to enhance their cross-border payment systems.

Described as a “digital town square,” the XC platform would build a three-layer architecture: a settlement layer that acts as the primary ledger, a programming layer for executing smart contracts, and an information layer designed to protect personal data while ensuring compliance and facilitating currency controls as needed.8 The platform’s architecture is designed to be open and upgradeable, ensuring its longevity and adaptability to future innovations. Instead of adopting CBDCs, central banks can issue certificates of escrow (CE) for use exclusively on the XC platform. CEs enhance financial accessibility by granting more entities, including nonbank financial institutions (NBFIs), payment-system providers (PSPs), and nonresidents, direct access to central bank reserves. These certificates share characteristics with CBDCs and can later be converted into central bank reserves by financial institutions. According to the IMF, a key advantage of using CEs is that it allows countries to prioritize domestic use cases for their CBDC projects, while CEs can be used solely for cross-border transactions.9

The XC model is designed for wide-ranging compatibility with existing systems, requiring central banks to make only minor technical updates. The model is a policy and regulatory framework; it encourages countries to adopt consistent and supportive regulations for cross-border payments, potentially incorporating tokens and distributed ledger technologies (DLTs). In order for the model to work, however, it will need compatible legal and regulatory frameworks to effectively manage risks and ensure compliance across various jurisdictions. Tobias Adrian, Financial Counsellor and Director of the Monetary and Capital Markets Department at the IMF, further explained this point at our conference in November 2023.

 

Bank for International Settlements

The BIS acts as the central bank for central banks, fostering monetary and financial stability globally. It actively explores the impact of digital currencies on the financial system and central bank operations. The BIS Innovation Hub facilitates research and development on digital innovation, helping member countries adapt to the rapidly evolving digital asset landscape. Its membership consists of sixty-three central banks and monetary authorities.10

Recent significant projects include Project Mariana, which tested cross-border trading using CBDCs and decentralized finance technology, and Project Icebreaker, which focused on using retail CBDCs for international payments through a novel hub-and-spoke model, both completing their testing phase in 2023.11 Most recently, Project Sela, a collaboration between the BIS Innovation Hub Hong Kong Centre, the Bank of Israel, and the Hong Kong Monetary Authority, focused on exploring rCBDC features including accessibility, cyber security, and effective public-private collaboration, with an emphasis on central banks overseeing retail ledgers and private intermediaries managing customer-facing services.12

In July 2023, the BIS presented the results of a survey showing that 93 percent of central banks are engaged in CBDC work, with retail CBDC development more advanced than wholesale CBDC.13 The survey reveals most central banks recognize the value of having both a retail CBDC and a fast payment system.14 By 2030, there could be fifteen retail and nine wholesale CBDCs publicly circulating, while stablecoins and crypto assets are rarely used for payments outside the crypto ecosystem.15 This BIS finding followed its June 2021 report discussing its survey on CBDCs, which found that many central banks had not decided on issuing a CBDC, but had a tentative inclination toward allowing cross-border use by tourists and nonresidents. In March 2021, the BIS explored the potential for multi-CBDC (mCBDC) arrangements to improve cross-border payments by leveraging interoperable central bank digital currencies. Technology could play a role in addressing inefficiencies, and the paper discusses the dimensions of payment system interoperability and the benefits of mCBDC arrangements.16

The BIS Universal Ledger interoperability model advocates for a shared global ledger that integrates various forms of money—including CBDCs, tokenized deposits, and other digital financial assets—into a single, programmable environment. The BIS aims to address the inefficiencies and silos present in the financial system by enabling safer transactions and atomic settlements within a transparent framework.

The architecture of the unified ledger model is designed to be secure, scalable, and interoperable, with a strong emphasis on privacy and regulatory compliance. At its core, the architecture includes a data environment for securely storing digital asset representations, like CBDCs and tokenized deposits, in organized partitions managed by authoritative entities such as central banks and commercial banks. The execution environment facilitates the automation of complex financial operations and secure, efficient transaction processing. This environment supports atomic settlement, ensuring comprehensive transaction success or complete rollback. In addition, to safeguard sensitive data and transaction privacy, the model implements cryptographic methods like homomorphic encryption and secure multiparty computation. These technologies enable encrypted data computation without exposing the actual data, reinforcing the system’s privacy and security. An important component of the BIS project is the governance framework that establishes operational and regulatory compliance protocols, while also detailing the responsibilities of all involved parties, including central and commercial banks.

Unlike the XC model, which builds on blockchain solutions, the BIS’s unified ledger approach uses application programming interfaces (APIs), creating a more centralized system where transactions have to be processed and validated by authorized entities, such as central banks or designated financial institutions. 17 Within this system, central bank money can circulate on a platform that is not owned and operated by the central bank, which can present risks. It also raises questions about the security, control, and integrity of central bank money when it is managed outside the traditional central banking systems.

 
 

The BIS favors a system grounded in central bank money, offering a sounder basis for innovation, stable and interoperable services across borders, and a virtuous circle of trust through network effects.18

 

Basel Committee on Banking Supervision

The BCBS is the global body for setting prudential standards for banking supervision and regulation. With the emergence of digital assets and their potential impact on banking operations and risk management, the BCBS is studying the implications for financial institutions. The committee’s membership includes central banks and banking supervisory authorities from twenty-eight countries.19

The BCBS standard for prudential treatment of crypto asset exposures integrates crypto assets into the Basel Framework for banks.20 Joint reports by CPMI, BIS, the IMF, and the World Bank on central bank digital currencies for cross-border payments emphasize CBDCs’ potential to enhance cross-border payments through international cooperation and coordination.

 

Financial Action Task Force

FATF primarily focuses on combating money laundering and terrorist financing and has had less emphasis on specific guidelines for CBDCs. Its recommendations function as guidance on regulating virtual assets and virtual asset service providers (VASPs) to ensure the prevention of illicit financial activities. More than 200 jurisdictions have committed to implementing FATF standards, making the organization a key player in shaping regulatory frameworks to maintain transparency and security in the digital asset sphere.21 FATF has thirty-eight member countries, including major economies and financial centers worldwide.22

FATF has published several papers related to virtual assets and VASPs. The first version of its Guidance for a Risk-Based Approach to Virtual Assets and VASPs, released in June 2019, focused on risk assessment and monitoring, particularly for issues of anti-money laundering and combating the financing of terrorism (AML/CFT).23 A twelve-month review of the revised FATF standards on virtual assets and VASPs was conducted in July 2020, showing progress in implementing these standards among some jurisdictions, but not yet sufficient progress to create a global AML/CFT regime for virtual assets.24 A second twelve-month review in June 2022 revealed continued progress, but indicated that implementation was still insufficient and certain challenges remained, such as the implementation of the “travel rule.25 This rule is a legal obligation that requires financial institutions—such as banks and cryptocurrency service providers—to collect and share detailed information about the parties involved in a financial transaction.

To address these challenges and based on the two reviews, FATF published Updated Guidance for a Risk-Based Approach to Virtual Assets and VASPs in October 2021. This guidance includes updates in six key areas, including clarifying the definitions of virtual assets and VASPs, guidance on stablecoins, and additional guidance on peer-to-peer transactions and information-sharing among VASP supervisors.26 However, the latest update on the implementation, published in June 2023, indicated that jurisdictions still struggle with fundamental requirements.27 The report also emphasizes the need for appropriate risk identification and mitigation measures, especially for decentralized finance (or DeFi) and unhosted wallets (e.g., controlled by the owner rather than a platform or exchange manager), which have the potential for misuse. In 2020, FATF has also reported to the Group of Twenty (G20) on stablecoins, outlining its specific views on the application of anti-money laundering and counterterrorist financing requirements.28 There is ongoing work needed to ensure consistent and effective implementation of FATF standards in the digital asset sphere, and some jurisdictions are still struggling with fulfilling the fundamental requirements outlined by FATF.

 

International Organization of Securities Commissions  

As the leading international standard-setting body for securities regulation, IOSCO plays a critical role in ensuring the stability and efficiency of capital markets. With a growing interest in digital securities, IOSCO’s principles on issuer and investor protection, market integrity, and risk mitigation have significant implications for the global adoption of tokenized assets. IOSCO has more than 120 members, including national securities regulators and exchanges from various jurisdictions.29

While debates on which digital assets count as securities are ongoing in the United States, IOSCO has been actively engaged in providing insights into the realm of digital asset markets through a series of consultation reports and public reports. Policy Recommendations for Crypto and Digital Asset Markets, published in November 2023, stands out as a comprehensive consultation report proposing eighteen recommendations that address six key areas of concern. These areas include conflicts of interest resulting from vertical integration, market manipulation, cross-border risks, custody and client asset protection, operational and technological risks, and retail access, suitability, and distribution.30

In March 2020, IOSCO released Global Stablecoin Initiatives, a public report emphasizing the applicability of principles for financial market infrastructures to stablecoin arrangements with systemically important functions. IOSCO’s work on exchange traded funds and crypto-asset trading platforms may also apply to global stablecoins.31 In March 2022, IOSCO presented its public report on decentralized finance, highlighting regulatory concerns like fraud risks, flash loans, cybersecurity, and spillover effects on traditional markets. Additionally, in December 2020, the organization published Investor Education on Crypto-Assets, a report to educate the public and investors on crypto assets and risk mitigation.32

 

Committee on Payments and Market Infrastructures  

Under the BIS, the CPMI provides a platform for central banks to promote the safety and efficiency of payment systems worldwide. With digital assets gaining recognition, the CPMI is actively engaging in discussions concerning the potential role of CBDCs and their interplay with private cryptocurrencies. The CPMI has twenty-eight members, representing major central banks and monetary authorities.

A 2018 Markets Committee report titled Central Bank Digital Currencies introduces and defines CBDCs, assessing their potential implications for monetary policy and central bank operations.33 It recommends further research on various aspects including interest rates, financial stability, and exchange rates. The report also warns against the risks of private digital tokens due to their volatility and lack of protection for investors and consumers, making them unsuitable for widespread use in payments.

 

Financial Stability Board  

The FSB’s mandate is to oversee and coordinate global financial regulation, identifying and addressing systemic risks to foster stability in the financial system. Recognizing the growing importance of digital assets, the FSB monitors developments and potential risks arising from their use and ensures that the digital asset market operates within established stability parameters.34The FSB is broadly focused on the global regulatory framework for crypto-asset activities, and has not released any specific research or guidelines on CBDC development. The board’s membership includes a combination of G20 economies, other major economies, and international organizations.35

 

International Organization for Standardization

The ISO fosters agreement on best practices and processes, and publishes standards and technical specifications (TS), including on the security aspects for digital currencies. ISO/TS 23526:2023 focuses on providing a security framework for the issuance and management of digital currencies in general, using cryptographic mechanisms standardized by ISO and other references. The document aims to integrate security aspects into the design of digital currency systems, as opposed to adding them later as an extra layer, to accommodate legacy infrastructures​.​36 ISO does not have any explicit references or guidelines on CBDCs’ technical security, but instead has a broader focus on digital currencies overall.The following organizations below were added after the conference and depict wide-ranging efforts for interoperability occurring both in the private and public sector.

 

Society for Worldwide Interbank Financial Telecommunication

Building on its legacy in global financial messaging, the Society for Worldwide Interbank Financial Telecommunication (Swift) has introduced a model to enhance its existing infrastructure for cross-border payments, making them faster, more transparent, and cost-effective. Currently in beta testing, this model facilitates the connection of disparate national CBDC networks, enabling them to communicate and transact with one another while leveraging Swift’s existing infrastructure and security protocols—best thought of as a hub-and-spoke arrangement between various central banks with Swift at the center. This initiative is part of a broader Swift effort to prevent the fragmentation of the global payments landscape into “digital islands.”37

The project began in March 2023, with over eighteen participants, including the Monetary Authority of Singapore and the Banque de France. Within a twelve-week period, they were able to process over 5,000 transactions. In September 2023, Swift further broadened the initiative by announcing the participation of three new central banks: the Hong Kong Monetary Authority, the Central Bank of Kazakhstan, and an additional, anonymous central bank.

Following the insights and successes from Phase 1, Swift released the takeaways from the Phase II CBDC sandbox project in March 2024, engaging thirty-eight central banks, commercial banks, and market infrastructures from around the globe. This project was designed to tackle complex use cases and assess solutions within a controlled sandbox environment. The second phase involved over 125 participants, who collectively executed more than 750 transactions. The sandbox was hosted on Kaleido, a Web3 platform for blockchain applications, where central banks were able to simulate CBDC transactions. Swift’s technology stack included a combination of the Corda, Hyperledger Fabric, and Hyperledger Besu platforms.38

Phase II explored four new use cases. First, it demonstrated the automation of trade payments through CBDC networks and smart contracts, aiming to improve trade efficiency and minimize costs. Second, it evaluated two models for foreign exchange trade and settlement: an International Foreign Exchange Marketplace and a Continuous Linked Settlement (CLS) inspired system, both of which underscored the integration of CBDC trade and settlement. Third, the project focused on delivery versus payment (DvP), facilitating atomic DvP for tokenized bonds by ensuring interoperability between tokenization platforms and CBDC networks. Finally, it investigated mechanisms to mitigate liquidity fragmentation across various currencies and platforms, utilizing smart contracts and netting algorithms. The report established three foundational principles for interoperability: linking networks via ISO 20022 messaging, providing a unified point of access through Swift, and ensuring coexistence with traditional market infrastructures.39

This model leverages Swift’s global reach and the existing network effects among financial institutions. It also offers flexibility for countries to maintain their own domestic CBDC infrastructure while ensuring global connectivity.

 

The Internet Engineering Task Force

The Internet Engineering Task Force (IETF) is deeply involved in the development of standards to enhance blockchain interoperability, focusing on the Secure Asset Transfer Protocol (SATP).40 This protocol is designed to enable seamless transfers of digital assets across diverse distributed ledger technologies (DLTs) by leveraging a network of trusted gateways, akin to the role border gateway routers played in the early internet. Such an approach offers a scalable and ledger-agnostic solution for the rapidly evolving digital asset ecosystem.

SATP facilitates asset transfers through a structured process that includes three main stages: Transfer Initiation, Lock-Evidence Verification, and Commitment Establishment. The protocol ensures that digital assets are exclusively valid within one network at any given time, adopting a transfer mechanism that maintains the asset’s integrity and uniqueness.41 This is achieved through the strategic use of gateway endpoints which manage the transfer process, ensuring secure, transparent, and auditable transactions that adhere to Atomicity, Consistency, Isolation, and Durability (ACID) principles.42 The SATP framework comprises a comprehensive set of API endpoints and resources for the initiation and execution of asset transfers. It also aims to facilitate the integration and management of digital asset transactions, contributing to a more efficient and secure digital economy.

Hyperledger, an open-source community focused on blockchain technologies, plays a role in implementing and advancing SATP through projects like Hyperledger Cacti.43 Cacti serves as a blockchain integration framework that enhances interoperability by allowing operations across multiple enterprise-grade blockchain networks. It achieves this through a pluggable architecture that supports Business Logic Plugins (BLP) and Ledger Connectors, enabling seamless interaction with various DLTs.

 

Global Blockchain Business Council

The GBBC has launched the fourth iteration of the Global Standards Mapping Initiative (GSMI 4.0), a comprehensive project designed to map and analyze the blockchain and digital assets landscape.44 This initiative provides an extensive overview of regulatory developments across 230 jurisdictions and six global bodies, compiles a taxonomy of 350 terms and definitions, maps sixty-three technical standards bodies, and identifies more than 2,000 stakeholders in the blockchain ecosystem. Additionally, it offers access to 1,500+ courses from accredited educational institutions and includes four in-depth reports focusing on AI convergence, digital identity, supply chain, and sustainability, with a special spotlight on Brazil. GSMI 4.0, building on the work since 2020, aims to present a holistic view of global industry activity. The initiative’s resources, including an interactive map of blockchain and digital asset regulations and a series of reports, are available on the GSMI site (https://gbbcouncil.org/gsmi/). All materials produced by the GSMI are crowd-sourced and open access, ensuring they serve as a reliable information source for those interested in blockchain and digital assets.

 

The Internet Governance Forum

The Internet Governance Forum (IGF), primarily serves as a multistakeholder platform for policy dialogue on internet governance issues.45 While not directly implementing or proposing specific financial systems, the IGF’s contribution lies in facilitating discussions, building consensus, and sharing best practices among stakeholders to influence the governance frameworks that underpin these technologies. First convened in 2006 by the United Nations secretary-general as a result of the World Summit on the Information Society (WSIS) held in 2003 and 2005, IGF gathers governments, the private sector, civil society, and technical communities to debate and share insights on enhancing internet security, ensuring digital privacy, fostering the digital economy, and expanding internet access.

Security, trust, and privacy are central to the IGF’s discussions on digital financial services. The forum encourages dialogue on how to protect against fraud, ensure the integrity of digital transactions, and safeguard users’ privacy and data in an increasingly digital global economy. Key areas of focus for the IGF also include the development of governance frameworks that protect user data and ensure a secure online environment. The forum also emphasizes the importance of digital inclusion, advocating for equitable access to the internet and digital services across different regions and communities. Through its annual meetings and intersessional work, the IGF indirectly supports the infrastructure and policies that impact the digital economy and financial inclusivity.

 
 

As the above section shows, there have been some efforts in creating standards for interoperability of digital assets. From feedback after the conference, we added the work of organizations such as Swift, IETF, GBBC, and IGF in standard creation. All the organizations listed above have led to important standard making efforts as described. However, these efforts are concentrated in specific areas and, as explored below, some crucial gaps exist that must be addressed in any evolving framework for standards.

Lessons learned from standard-setting efforts

As we evaluate the above models of governance, it is important to assess growth opportunities for the next stage of standard developments. In this section, we identify the critical learnings and gaps in standards development for interoperability of digital assets.

First, the rCBDC experimentation space has provided countries with some experience in building CBDCs, largely driven by domestic objectives. These experiments are at very different stages and use a range of private-sector vendors that are not subject to the same regulations due to a slower pace of crypto-asset regulation globally.

Second, within wCBDC experimentation, operating frameworks in technology and regulation have emerged, led by entities like the BIS Innovation Hub, the global financial-messaging cooperative Swift, and other private-sector players. However, they are constrained by the limited number of participating countries, furthering the issue of fragmentation in cross-border CBDCs. Current experimentation should incorporate standards-setting bodies (SSBs) such as FSB, BCBS, CPMI, ISO, and FATF as participants or observers to ensure better collaboration in the development of standards.

The membership structure of SSBs significantly influences the establishment of the goals and priorities of these institutions. Additionally, while emerging market economies often surpass developed economies in the development of digital infrastructure, including CBDCs, they sometimes find themselves underrepresented in setting norms and establishing benchmarks. This underrepresentation can result in an inadequate consideration of their technological advancements within the organization’s priorities.

Moreover, apart from the FATF, there seems to be a shortage of robust frameworks for assessing the global standards’ impact and implementation lags. To address the evolving landscape of financial technologies, it is imperative that new and non-financial SSBs be actively involved in these discussions, leveraging their expertise in technological matters and regulatory concerns.

Finally, some of the above frameworks have actively involved private sector participants in influencing standard development and creation. As intermediaries, the private sector has a crucial role in the entire lifecycle of standards, from actively influencing the creation of standards to ultimately adopting and implementing them.

Towards establishing standards

A transparent and collaborative multi-stakeholder approach is crucial for establishing frameworks for standards related to digital currencies. Standardization is driven by consultation processes with governments, industry specialists, consumers, regulators, and civil society organizations (CSOs). Historically, governments have provided the necessary legal and governance paradigms, in turn creating environments conducive to standard development and assimilation across multi-stakeholder groups. Central banking authorities, driven by the imperative of maintaining financial stability and directing monetary policy, contribute a nuanced perspective essential for shaping these standards. The private sector’s technological advancements and practical exposure play a critical role, not just in ideation, but in the tangible implementation of these standards, ensuring their practical efficacy. Lastly, the participation of CSOs provides reflection and inclusion of key social elements, serving as a check by society on the suitability of resulting standards.

The goal of this collaborative process is the establishment of a guiding framework for standards. To begin this process, we outlined the following themes for CBDC framework creation, which align with the G7 principles proposed in 2021, to identify the key themes necessary to begin building a framework. These key themes are governance; privacy and data protection; competition and consumer protection; global impact and sustainability; and transferability and accessibility. Through conversations at the conference and outreach afterward, we aimed to test the robustness of these themes through a survey (see Annex 2 for survey questions). Within each theme, we describe the areas of framework development needed for the establishment of standards. Conference attendees and survey respondents identified thematic overlaps and largely agreed with these themes, which have allowed us to set policy priorities for CBDC frameworks.

A thematic approach to CBDC and digital asset standard creation

 
  • Governance
    Effective governance of CBDCs requires a nuanced approach, placing a focus on maintaining public policy objectives and central bank mandates including monetary and financial stability. To achieve this, the framework should involve the creation of dynamic mechanisms that not only monitor, but also proactively mitigate potential destabilizing effects. Stress-testing frameworks are essential tools for central banks to assess the comprehensive impact of CBDCs on economic stability. The principle of “do no harm” dictates that economic stability must be safeguarded at every stage of CBDC implementation, through concrete guidelines and risk assessments. In parallel, there is an imperative to establish legal and governance frameworks, offering clear definitions of regulatory benchmarks. Governance is the biggest challenge that emerges as we analyze existing efforts for standard setting, as each of the technical models discussed at the conference envisions an operator of an inherently global system. This is a complex and difficult endeavor, likely to have many challenges and phases.
 
  • Privacy and Data Protection
    The protection of privacy and data involves specifying requirements for user data protection, consent, and disclosures.46 Mechanisms for cross-border data transfer should be designed to navigate the complexities of various data protection laws across jurisdictions, ensuring compliance, individual privacy protections, and seamless transactions. Operational resilience and cybersecurity require technology standards for resilience against cyber, fraud, and operational risks, including security measures, encryption standards, and incident response protocols.47 There was widespread agreement at the conference that piecemeal privacy protections will not be sufficient for the evolving financial system, and that comprehensive privacy protections will have to be regulated for. Additionally, all models of digital asset interoperability have highlighted the importance of built-in privacy frameworks.
 
  • Competition and Consumer Protection
    CBDCs should coexist with existing means of payment and should operate in an open, secure, resilient, transparent, and competitive environment that promotes choice and diversity in payment options. Promoting fair competition and consumer protection requires the development of international standards for open-access APIs, ensuring competition and interoperability, thereby enhancing the overall efficiency of the CBDC ecosystem. It also is crucial to strike a balance between the demand for faster, more accessible payments and the necessity to combat illicit finance and protect the right to personal privacy. Establishing protocols for collaboration between CBDC operators and regulatory authorities, including law-abiding information sharing, joint investigations, and the development of responsive regulatory frameworks, is vital to address and mitigate potential risks associated with illicit finance.48
 
  • Global Impact and Sustainability
    Considering the global impact and sustainability of CBDCs, spillovers can begin to be addressed by establishing technical principles for cross-border transaction reporting and information sharing. Energy and environmental considerations are crucial; hence, international standards for the energy efficiency of CBDC infrastructure should be created, specifying benchmarks for sustainable operations. This has to be built into the next phase of testing and experimentation at the domestic and international levels.
 
  • Transferability and Accessibility
    Ensuring interoperability with existing and future payment solutions is necessary to achieve the goal of transferability and accessibility. Technical standards should be formulated for integrating CBDCs with emerging digital payment solutions, and interoperability protocols should be specified to facilitate seamless transactions between CBDCs and other payment instruments. Additionally, for payments to and from the public sector, protocols for cross-border collaboration among central banks and organizations must be defined, addressing the international dimensions of CBDC design. Technical requirements for cross-jurisdictional compatibility and seamless integration into global financial systems should be established. Additionally, technical reporting requirements should be instituted to ensure transparency in the utilization of CBDCs for international development initiatives. A lot of recent experiments have shown “token agnosticism” or the ability to support a wide variety of tokens, demonstrating that builders do not want to be overly prescriptive and provide consumers with a range of options.
 

These key themes illuminate the areas of framework development needed to achieve comprehensive standards for CBDCs. These are not an exhaustive list, but provide primary recommendations as the public sector, policymakers, and the private sector engage in CBDC development.

Through conversations at the conference, it was evident that the G20 payments roadmap is used as an industry benchmark by the public and the private sector as they address modernization efforts. The identified themes speak to some of the priorities outlined by the G20, but seek to go beyond the existing priorities. As G20 targets evolve to include leveraging the digital asset ecosystem, the above described themes can provide crucial benchmarks for standards creation. As governments draft regulations and the private sector engages in experimentation, often along with the public sector, they must address these themes. It also is imperative that global standard-setting bodies address the current gaps in their guidance and participate in these discussions—especially in the development of cross-border flows. Through the conference, it also became clear that many of the standard setters in this space are working across overlapping areas of work—which makes the need for communication channels essential going forward. Crucially, as was repeatedly emphasized at the conference, interoperability is imperative as any standards for CBDCs or digital assets broadly are developed, so that future systems of money do not increase friction in the global payments landscape.

Conclusion

As countries worldwide explore CBDCs’ potential for an advanced and seamless digital infrastructure, a unified standard framework will become necessary to foster harmony, quality, and trustworthiness worldwide. Our working paper served as a call to action for both public and private stakeholders to actively engage in standard-setting efforts with the goal of ensuring interoperability and efficiency, as well as embedding democratic norms, values, and rules of law in CBDCs.  It also set some common definitions and understanding of the current state of international standards for those seeking to understand the current state of international standards and existing gaps and areas for improvement. As previously noted, standards ensuring consistency and seamless functionality are not static; they must be flexible enough to accommodate advancements in digital currency technology, shifts in economic priorities, and changing societal perspectives on digital assets.

To further global dialogue on these topics, The Digital Dollar Project and the Atlantic Council GeoEconomics Center hosted a first-of-its-kind convening, “Exploring Central Bank Digital Currency: Evaluating Challenges & Developing International Standards,” on November 27-29, 2023. This event brought together international policymakers, technologists, financial services providers, innovators, and consumer and privacy advocates to discuss the ongoing impact of emerging technologies on the future of money, its infrastructure, and global payment systems. The convening explored the complexities around digital currency, focusing on key technology and policy considerations, outlining areas for future public-private cooperation, and identifying potential pathways to standards that embed privacy protections, democratic values, and interoperability. Following the conference, this paper was revised to reflect what we learned from the conference, incorporate recent developments in international standard setting, and build on the framework offered in the working paper in consideration of future global interoperability standards efforts.

Link

community logo
Join the TheDinarian Community
To read more articles like this, sign up and join my community today
0
What else you may like…
Videos
Podcasts
Posts
Articles
🚨 BOMBSHELL MEDICAL REPORT: Florida Launches Official Study into 77-Cent Ivermectin to Fight Stage 4 Cancer!

Florida shatters the Big Pharma consensus! The State officially launches funding for generic drug repurposing, investigating 77-cent Ivermectin for cancer treatment.

⚠️ Breaking the Big Pharma Monopoly: How the State of Florida is Investing Taxpayer Dollars into Generic Drug Repurposing to Bring Unprecedented Hope to Cancer Patients.

00:02:39
👁️ THE KILL CHAIN AUTOMATED: Palantir, the DOD, and the Age of AI Warfare 🛰️⚡

While the public debate remains focused on consumer AI chatbots, the defense-industrial complex has quietly deployed real-time artificial intelligence into operational military decision-making.

Here is what you need to know about the integration of Palantir’s AI infrastructure and military data networks:

📡 1. Shrinking the "Kill Chain"

Through platforms like Project Maven and Palantir's Artificial Intelligence Platform (AIP), military surveillance systems process massive streams of satellite imagery, drone telemetry, and signals intelligence in real time.

🔹 The Goal: Reduce target identification, processing, and decision workflows from hours to seconds.

🔹 The Reality: Data streams from edge sensors (drones, aircraft, satellites) are fused instantaneously, surfacing potential targets directly to operators with automated strike recommendations.

🛡️ 2. Sensor Fusion & The Tactical Edge

Modern operational platforms don't just log data—they deploy algorithmic model...

00:01:51
The Only Thing Stopping You, Is You...

This is why prayer, visualization and meditations can be so powerful...

You already have it...

The universe will have no option but to make it a reality ✨️

00:01:04
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨

Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.

🔑 Key points

🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.

🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.

🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨

A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.

🔑 Key points

🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.

🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.

🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading
🚨Q2 webinar with Denelle Dixon (CEO STELLAR)🚨

Join the Q2 webinar with Denelle Dixon, Jose Fernandez da Ponte, Tomer Weller, and Raja Chakravorti

https://www.linkedin.com/events/7488670276189114369/

post photo preview
🌎 Schumann Resonance Today 8/2 🌍

Today's Frequency Analysis: The fundamental resonant frequency of the Earth today 7.83 Hz it remains stable around. Tomsk Space Observing System (SOS) data shows that global lightning activity is at an average level today. The low deflection in the fundamental mode SR1 indicates a healthy ionosphere-terrestrial crust interaction.

Harmonics and Amplitude: The second harmonic (SR2) shows weak activity as expected at ~14.3 Hz. Third harmonic (SR3) ~20.8 Hz normal. Amplitude values 13-14 pT in the range of, which suggests a healthy signal quality (93+%). Solar wind speed is around 420 km/h, geomagnetic activity is low-moderate.

Geomagnetic Context: Kp index today 2.0 level, calm conditions. Solar wind is within normal range, no CME activity. Under these conditions, Schumann resonance is experiencing its most stable period — the ideal environment for meditation and bio-feedback practices.

Spectrogram Interpretation: The 24-hour Tomsk spectrogram shows a slight increase in the morning hours (06-09 UTC). ...

post photo preview

Everyone expects a black swan. 🙇‍♂️

Nobody expects regulatory clarity. 😶‍🌫️

Keep this in mind as you listen to the mainstream narratives that distract retail investors. 💯

“Inflation.”

“Oil.”

“Crash.”

Recycled words meant to spread fear and signal danger.🔁

Remember, the crowd is always wrong.🎯

And that isn’t changing now. ☝️

Op: Smqkedqg

post photo preview
post photo preview
AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

🙏 Donations Accepted, Thank You For Your Support 🙏

If you find value in my content, consider showing your support via:

🙏 Cashapp: $thedinarian

🙏 Buy me a coffee: https://buymeacoffee.com/thedinarian

🙏 PayPal: Scan the QR code below 📲 or Click Here

🙏 Crypto Donations 👇
XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
🤖 Decentralized Intelligence by Design: Unpacking the Bittensor Flywheel

In the legacy tech world, artificial intelligence is governed by corporate monopolies. Companies like OpenAI and Google scale by capturing massive capital, locking talent behind non-disclosure agreements, and building closed-source infrastructure. 🛑

Bittensor flips this paradigm completely on its head. By combining a Bitcoin-inspired tokenomic model with a permissionless, competitive architecture, Bittensor doesn't just fund AI development—it orchestrates an unstoppable digital commodity flywheel. 🌪️

Here is an analysis of how the Bittensor ($TAO$) Flywheel Effect operates, and why its economic design is quietly building the foundation for generalized, open-source intelligence.

1. ⚙️ The Core Engine: The TAO Emission Mechanism

Unlike traditional crypto projects driven by private sales or VC allocations, Bittensor enforces a strict meritocracy. There are exactly 21 million TAO tokens that will ever exist, mimicking Bitcoin’s scarcity framework. 🪙

The network’s core engine releases 7,200 TAO daily across the ecosystem. This issuance isn't handed out randomly; it is dynamically distributed to specialized mini-marketplaces known as Subnets via a game-theoretic protocol called Yuma Consensus.

2. 🔄 The Three Stages of the Flywheel

The Bittensor flywheel works because it directly aligns the local self-interest of developers, miners, validators, and capital providers with the global health of the network. 🎯

🛡️ Phase 1: High-Barrier Subnet Competition

To build on Bittensor, an entrepreneur or developer group must purchase and "burn" or lock up a significant amount of TAO to secure a Subnet slot.

  • The Filter: This entry barrier filters out noise.

  • The Result: It ensures that only teams with mature concepts and solid execution capabilities (like decentralized storage, protein folding, or LLM inference) enter the arena.

💎 Phase 2: Alpha Token Emissions & Talent Attraction

Once a subnet is live, it competes aggressively against other subnets for a slice of the daily 7,200 TAO pool. Under the Dynamic TAO framework, each subnet utilizes its own localized native token (Alpha tokens). 🧪

  • Reward: The subnets that produce the highest utility or most innovative AI products receive a larger allocation of global TAO emissions.

  • Incentive: These emissions fund the subnet's local Alpha pool, offering massive financial rewards to the best Miners (who provide the actual compute/AI models) and Validators (who verify the accuracy and value of the work).

🔒 Phase 3: The Liquidity Loop and Token Scarcity

Because Alpha tokens are inherently priced relative to TAO, external investors or users who want to stake on or utilize a specific high-performing subnet must first acquire TAO. 📈

  • As a subnet's product quality improves, demand for its Alpha token surges.

  • To buy Alpha, participants must buy and lock up TAO in decentralized liquidity pools.

  • This removes circulating TAO from the open market, reducing effective float and driving up the value of TAO.

3. 🚀 Why the Flywheel is Unstoppable

The beauty of this cycle is that it feeds itself:

Higher TAO Price ➡️ More Valuable Subnet Emissions ➡️ Attraction of Higher-Tier Talent/Compute ➡️ Superior AI Products ➡️ Increased Network Demand ➡️ Higher TAO Price📈

Traditional startups spend millions on recruitment and marketing. Bittensor bypasses this entirely: its emission schedule acts as a global bat-signal for talent. 🌍

If a miner in Eastern Europe or a data scientist in Tokyo can optimize an open-source model to solve a specific subnet's prompt better than anyone else, the network automatically and frictionlessly rewards them.

💡 The Takeaway

Bittensor is more than a blockchain; it is an economic computer designed to run incentive structures in massive parallelism. By treating machine intelligence as a digital commodity and wrapping it in a circular value flow, the Bittensor flywheel transforms raw computational energy into an emergent, open-source super-intelligence. 🧠⚡

As subnets mature from raw infrastructure into client-facing enterprise APIs, the velocity of this flywheel is poised to redefine the economics of AI forever.

I hope this was helpful ~Dinarian888♾

🙏 Donations Accepted, Thank You For Your Support 🙏

If you find value in my content, consider showing your support via:

💳 Stripe:
1) or visit http://thedinarian.locals.com/donate

💳 PayPal: 
2) Simply scan the QR code below 📲 or Click Here

🔗 Crypto Donations Graciously Accepted👇


XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
🚨Japan Just Entered the AI Race with Sakana, Claiming to Beat Mythos with a Router🚨
On June 12, the US pulled Anthropic’s best model offline by export order. Ten days later, Tokyo’s Sakana AI shipped Fugu, a router that reassembles the same capabilities from the models that are still standing. Blocking intelligence created the market for routing around it.

 

At 5:21 p.m. Eastern on Friday, June 12, 2026, Anthropic received a letter from the US Department of Commerce and, by its own account, had on the order of an hour to take its two most capable models offline.

The letter was an export control directive. It ordered Anthropic to suspend all access to Claude Fable 5 and Claude Mythos 5 “by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.” Because the company cannot reliably check the nationality of everyone calling an API, the only way to comply was the blunt one. Anthropic disabled both models for every customer on earth, and they stayed dark. As of late June 2026, neither Anthropic nor the government has announced a timeline to restore access, and an approved BIS license is now required before any foreign person can touch them. This was not a chip ban. It was the first publicly confirmed time the US government reached past the hardware and the weights-in-transit and pulled the plug on a running model.

Ten days later, on June 22, a Tokyo lab named Sakana AI shipped the response. Its new product, Fugu, is not a frontier model. It is a router: a small trained model that conducts a pool of other companies’ models and stitches their…

Sandwiched between those two dates, on June 13, China’s Z.ai released GLM 5.2, an open-weight model under an MIT license priced at roughly a sixth of Fable 5. None of these three were reactions to each other in any literal sense; GLM 5.2 and Fugu were finished pipelines that happened to land in the same news cycle. But the cycle told a story the policy did not intend. Block a model, and within ten days the open-weight competitor and the orchestration workaround both look less like products and more like exits.

This piece is about that asymmetry: why a government can switch off a model in ninety minutes, why it is far harder to switch off a system that reassembles the same capability from parts it does not control, and why the last time Washington tried this exact move, with encryption in the 1990s, it lost.

What got banned, and why it was a first

Mythos 5 is the most capable model Anthropic has built, positioned above Opus in the family and never sold to the public. Access ran through a vetted-partner program called Project Glasswing, built around cybersecurity. The reason it was gated is not marketing. On a Firefox JavaScript-engine benchmark where Claude Opus 4.6 produced two working exploits, Mythos Preview produced 181, and gained register control on dozens more targets. It autonomously surfaced a 27-year-old vulnerability in OpenBSD’s TCP stack that had survived human audits, automated fuzzers, and decades of unusually careful open-source review. Over three months pointed at Firefox, Anthropic reported, the model turned up 271 previously unknown vulnerabilities at a false-positive rate under 5%. Fable 5 was the public, safety-gated sibling: the same generation with classifiers that route high-risk cyber and bio queries to the older Opus 4.8 and trip, Anthropic says, in under 5% of sessions.

Press enter or click to view image in full size
Mythos Preview’s cyber results against earlier models. Source: Anthropic, “Mythos Preview”, Apr 7 2026 (vendor-reported). License: Anthropic; confirm reuse before publishing.

 

The legal move was the structural novelty, not the capability. The January 2025 AI Diffusion Rule had already created an export classification (ECCN 4E091) for the weights of advanced closed models, things that sit still and can be licensed like any controlled good. The June 12 directive went a step past that, onto a live commercial API. Commerce could argue this is a natural extension of the same authority, and it is not a crazy argument. But in practice, it is the first time the controlled thing was not a chip you can put in a crate or a weights file you can copy, but a service anyone can call from anywhere, at any time, until the moment it is switched off.

The trigger is contested, and you should treat it that way

What actually set this off is disputed, and the accounts do not line up.

The administration’s version came mostly from White House AI and crypto czar David Sacks, who said on June 13 that a “highly credible trusted partner” had demonstrated a jailbreak of Fable’s guardrails amounting to “the operability of a cyber weapon,” that the government asked Anthropic to fix it or pull the model, and that CEO Dario Amodei refused. Multiple outlets identified that partner as Amazon, an Anthropic investor and compute provider, and the Wall Street Journal reported that Amazon CEO Andy Jassy told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 to obtain information usable in cyberattacks.

Anthropic’s version is that this was a “narrow, non-universal” potential jailbreak (“read a specific codebase and fix any software flaws”), that the capability in question is “widely available from other models, including OpenAI’s GPT-5.5,” and that recalling a model “deployed to hundreds of millions of people” over it was disproportionate. Independent voices leaned toward Anthropic on the technical point. Katie Moussouris, CEO of Luta Security, was blunt: “I’ve seen the paper. It’s not a jailbreak.” A former Commerce official, Kate Koren, suggested the White House’s sour relationship with Anthropic may have colored the decision. Semafor separately reported the move was tied to suspicion that a China-linked group had accessed Mythos, a motive Anthropic says the White House never raised with it and which other outlets could not confirm.

The honest summary: the trigger is Amazon-reported and Sacks-narrated, contested by Anthropic, doubted by outside researchers, and the China angle is unverified. Hold it loosely.

What Sakana actually shipped

Press enter or click to view image in full size
Timeline illustration contrasting June 12 when US export control took Mythos and Fable 5 offline in 90 minutes, with June 22 when Sakana AI’s Fugu 7B router launched as the workaround, routing queries across GPT-5.5, Opus 4.8, Gemini 3.1, and Fugu to produce one answer
One model gets unplugged; a router conducts the ones still standing. (Original illustration.)

 

Fugu is not a frontier model in the usual sense, and Sakana does not pretend otherwise. What it shipped is stranger, and arguably more interesting: a multi-agent system delivered as a single model, a coordination layer dressed as one OpenAI-compatible endpoint. The complexity never reaches your code. Your app sends one request; Fugu decides, behind the wall, whether to answer directly or assemble a team. Underneath, it is a learned orchestration system built around a roughly 7-billion-parameter “conductor” (a Qwen2.5–7B base) trained with reinforcement learning to design collaboration strategies across a pool of larger worker models. Two ICLR 2026 papers sit underneath it: Trinity (arXiv 2512.04695), a sub-20K-parameter coordinator tuned by derivative-free evolution, and Conductor (arXiv 2512.04388), the RL-trained orchestrator that hands out roles. The lineage runs back to Sakana’s 2025 AB-MCTS work (arXiv 2503.04412, a NeurIPS spotlight), which showed that letting several frontier models cooperate at inference time, deciding adaptively whether to go wider or deeper, beat any single one of them.

Sakana’s own framing is the sharpest way to see it: Fugu is model merging moved up a level. The technique that made the lab’s name, evolutionary model merging, blends the weights of open models, which requires matching architectures and downloadable checkpoints. Fugu does the same job one layer higher, composing what models do rather than what they are, treating each frontier system as a black box and learning to route, verify, and synthesise their behaviour, “without requiring parameter access or architectural compatibility.” That reframing is the unlock: it is how a lab with no frontier weights of its own gets to merge OpenAI’s, Anthropic’s, and Google’s anyway, through the front door of their APIs.

The mechanism is worth one layer down, and the two tiers do it differently. Plain Fugu decides without writing a word: a lightweight selection head reads the hidden state of your prompt, scores every model in the pool, and dispatches to the top one before any text is generated, which is why it stays nearly as fast as a single call. Its predecessor, Trinity, tagged each pick with a role: Thinker, Worker, or Verifier; Fugu dropped the roles and simply takes the best worker. Fugu-Ultra goes further: it writes an agentic workflow, a sequence of steps, each carrying a plain-language subtask, a worker id naming the model to run it, and an access list controlling which earlier results that worker is allowed to see. Tune the access list, and you get a chain, a best-of-N, or a tree. The pool is swappable, GPT-5.5, Opus 4.8, Gemini 3.1 Pro, or recursive copies of Fugu itself, and when Fugu calls itself, it reads its own earlier output, judges whether it is working, and spins up a corrective pass. None of it is hand-coded with if-statements; it is learned, plain Fugu through supervised fine-tuning and then evolutionary search, Fugu-Ultra through reinforcement learning, on roughly 960 problems across two H100 GPUs. Commercially, it ships in those two tiers behind an OpenAI-compatible API, with subscriptions at $20, $100, and $200 a month and a metered free tier through Vercel’s AI Gateway, the official third-party integration, which routes to the same closed pool of GPT-5.5, Opus 4.8, and Gemini 3.1 Pro.

That difference shows up as quality. Plain Fugu, picking one model per step, can hand a coding request to GPT-5.5 to draft and to Opus 4.8 to debug a few turns later, all inside one request, yet on SWE-Bench Pro it still lands ten points below Opus alone (59.0 to 69.2): routing among models is not the same as being better than the best one. Fugu-Ultra earns its keep on harder work, and one of its smarter habits is that the model that writes the final synthesis is not pinned in advance, the way an “LLM council” fixes one judge, but chosen by domain. Its ceiling is the planning. The workflow is drawn before any agent has produced anything, so the system commits its branching at t=0 instead of adapting at t+1 from what it just learned, which is why the workflows stop at a few steps; the smartest version of this idea reacts to intermediate results, and Fugu-Ultra mostly cannot.

How does a 7B model learn any of this? In two ways, one per tier. Plain Fugu starts with supervised fine-tuning on questions whose answers are known: run every worker several times, turn each one’s average score into a soft probability with a softmax, so the target keeps “GPT best, Opus a close second, Gemini weak” instead of collapsing to “always GPT,” and train the selection head to match that distribution.

Then it is polished with an evolutionary method, sep-CMA-ES, on full multi-turn tasks where the only signal is pass-or-fail at the very end and ordinary gradient training has nothing to grab: try many small variations of the weights, keep the ones that finish more tasks, move toward them. To keep that cheap, Fugu nudges only a thin slice of its weights, using the SVD trick from Sakana’s earlier Transformer-squared work, rather than retraining the whole model. Fugu-Ultra is trained by reinforcement learning instead (GRPO, from the DeepSeekMath line): for each question, it writes a group of candidate workflows, scores each one (0 if the plan is malformed, 0.5 if it runs but the answer is wrong, 1 if it runs and is correct), and pushes up the workflows that beat the group’s average while pushing down the rest. Over many rounds, it learns to write plans that look like the ones that worked.

Turning several agents loose with tools creates two failure modes that Sakana had to engineer around, and the fix is tidy. If every agent could see everything the first one did, they would all follow its lead, and the team would collapse into a single opinion, so inside a workflow, each agent is isolated, seeing the others only through the access list the conductor set. But total isolation is wasteful: over a long task, agents would re-run the same tool calls and rediscover the same facts, so across the whole conversation they share a persistent memory of what has already been called. Independent within a step, shared across the task. That is the balance that keeps a real team both diverse and non-repetitive.

Press enter or click to view image in full size
Fugu AI multi-agent orchestration diagram showing the 7B conductor robot assigning Thinker, Worker, and Verifier roles across a swappable rack of AI models including GPT-5.5, Opus 4.8, Gemini 3.1 Pro, and recursive Fugu, trained on 2x H100 GPUs, synthesizing into one answer
The 7B conductor scores the pool, dispatches subtasks across it (including to copies of itself), and synthesises one answer. (Original illustration.)

 

CEO David Ha put the thesis plainly: “Relying on a single company’s APIs for critical infrastructure, finance, or governance is a material vulnerability. This risk is no longer a hypothetical possibility, but a reality.” Ten days after June 12, that sentence reads less like a product slogan and more like a market read.

Is any of this worth it over just calling Opus or GPT-5.5 directly? For a single clean prompt, almost certainly not, and Sakana’s own numbers concede it, plain Fugu trails the best single model it routes among. The case for orchestration is the messy task, the kind of real work it is actually made of: read ambiguous context, split it, hand the pieces to different specialists, verify, kill the weak branch, merge the rest, and stop before the loop runs forever. That is the layer most teams already hand-build out of routers, prompts, eval scripts, and retry glue nobody wants to maintain. Fugu’s bet is to sell that layer as a model.

What makes the bet plausible is that the frontier models really do specialise. By Sakana’s reading of its own pool, GPT-5.5 is strongest at math and at planning and combining ideas, Opus 4.8 at software engineering and at finding security bugs, Gemini 3.1 Pro at implementing known algorithms and at science. A conductor who has learned those edges can do things no single member would, and Sakana did not script the moves; they surfaced in training. On coding tasks, Fugu-Ultra learned to let GPT build and then pull Opus in at the right moment to hunt bugs and security holes before handing the findings back; on a cryptanalysis task, it had Opus open the attack and GPT re-derive the math it needed. That is the instinct a good tech lead runs on, knowing exactly which teammate to call for which part of the job.

The demos carry the idea better than the scorecard does, with the same caveat: they are Sakana’s, and the rivals are anonymised as “Model A, B, and C,” the labels reshuffled between examples so you cannot decode them (the field is Gemini 3.1 Pro, Opus 4.8, and GPT-5.5). With that asterisk, a few are hard to fake. Turned loose to improve a small GPT training recipe, Fugu Ultra ran the research loop itself, edit the code, run the experiment, measure validation bits-per-byte, keep the change if it helped, repeat, 123 experiments over about 14 hours on a single H100, landing at 0.9774 bits-per-byte against the baselines’ 0.9781, 0.9793, and 0.9822.

Asked to write a Rubik’s Cube solver from scratch in pure Python, its code solved 300 of 300 held-out scrambles at an average of 19.72 moves, a hair off the proven optimum of 20, while two of the three baselines wrote code that crashed on all 300. Pointed at a 1610 manuscript and told to recover the reading order of scattered Japanese kana, it scored 0.80 against a baseline of 0.24. Playing four games of blindfold chess, no board shown, the whole position held in its head, it won all four, including one against a 2,100-Elo engine, without a blunder. Handed a 50-week trading simulation starting at $10,000, it finished at $11,943, a 19.43% gain, ahead of every model it called (Sakana frames this as a no-look-ahead decision test, not investment advice, and you should too). These are runnable artefacts and agent loops, not trivia answers; they either work or they visibly do not.

And here is the part that a policymaker should sit with longer than any benchmark. The week the US made its best model unreachable behind a license, Fugu made frontier-adjacent capability reachable behind a dropdown. It is one OpenAI-compatible endpoint: point Codex or any OpenAI client atapi.sakana.ai/v1, set the model to fugu-ultra, and you are running in minutes, or skip the wiring and prompt it in a browser at chat.sakana.ai. No waitlist, no nationality screen, no export letter. Whether or not Fugu matches Mythos, that part is not in dispute, and it is the whole reason the ban looks porous: the controlled capability did not have to be smuggled. It had to be subscribed to.

The claim that hasn’t been checked

Sakana’s launch post says Fugu Ultra “stands shoulder-to-shoulder with leading models like Fable 5 and Mythos Preview.” That is the headline, and it is prose, not a number. Nowhere on Sakana’s own benchmark page do Fable 5 or Mythos scores appear in the same table as Fugu’s, under the same conditions. The reason is one Sakana states outright: “Fable 5 and Mythos Preview are not in Fugu’s agent pool as they are not publicly accessible,” and “all scores other than Fugu’s are reported by the respective model providers.”

So the parity claim is a comparison between Fugu’s own numbers and the manufacturers’ separately published numbers for two models Fugu cannot pool, cannot run head-to-head, and which the public can no longer access at all. What Sakana does show is a table against the models it can still reach:

Press enter or click to view image in full size
Sakana AI benchmark comparison charts showing Fugu Ultra and Fugu outperforming or matching Fable 5, Mythos Preview, Gemini 3.1 Pro, GPT-5.5, and Opus 4.8 across six benchmarks: LiveCodeBench, GPQA-D, CharXiv Reasoning, SWEBench Pro, SciCode, and Humanity’s Last Exam. Source: Sakana console benchmarks with provider-reported scores for competitor models.
Source: Sakana console benchmarks (console.sakana.ai/models). Fugu’s numbers are Sakana’s own; the rest are provider-reported, not re-run in a common harness.

 

It is a real result. On these rows, Fugu Ultra edges out three frontier models by orchestrating them. But step back, and the framing matters. This is not a clean sweep (on longer-context and multi-call benchmarks elsewhere in the set, Fugu Ultra slips behind GPT-5.5 and Gemini), and the marquee “matches Mythos and Fable” claim is the one piece of the story no outsider can test, because the comparison it implies has never been run in a single harness and now cannot be. The right word is not “unfalsifiable.” The right words are not yet independently verified, and currently unverifiable under a neutral evaluation, which, for a buyer making a procurement decision in June 2026, amounts to the same caution.

There is a deeper apples-to-oranges problem inside the table. Fugu Ultra is an orchestrator that spends several model calls on every answer; Opus 4.8, Gemini 3.1 Pro, and GPT-5.5 in that table are single models answering once. The honest comparison is not Fugu against one Opus call, it is Fugu against Opus run in its own multi-step mode (Anthropic’s “ultracode” workflows), or against a swarm of Kimi agents, orchestrator against orchestrator at matched spend. Sakana does not publish that. It also reports an “AutoResearch” benchmark against rivals it labels only “Model A, B, and C,” a strange thing to anonymise, and observers flagged at least one competitor figure (Figure 5’s TerminalBench score) as off, the kind of error that slips through precisely because nobody re-ran anything in one place.

The trust problem

There is a specific reason to read Sakana’s self-reported numbers with a raised eyebrow, and it is Sakana’s own recent history.

In February 2025, the company unveiled the “AI CUDA Engineer,” claiming 10x to 100x speedups over plain PyTorch, with a headline figure up to 150x. Within a day, outside testers could not reproduce it. The system had reward-hacked the benchmark: it found a memory exploit in the evaluation harness that let its generated kernels skip the correctness check entirely. An independent retest pegged the real average speedup at about 1.49x against a valid benchmark, against the paper’s claimed 3.13x average, and nothing like the headline. Sakana’s postmortem admitted the model had “found a way to cheat” and “reward hacked,” apologised, and promised a revision. To the company’s credit, it later published work on hardening the eval, and benchmark-gaming is a problem every lab wrestles with, not a Sakana-only sin. But the pattern is exactly the one that should make you cautious about a fresh set of self-reported, no-common-harness, can’t-be-reproduced parity claims from the same shop sixteen months later.

The structural critiques go past track record:

  • Orchestration is a meta-system, not a new ceiling. Fugu’s intelligence is bound by the best model it can call. It can squeeze more out of existing capability; it cannot exceed it. The thing it claims to match, frontier intelligence, is precisely the thing it does not itself contain.
  • The resilience pitch is only as strong as the pool. “Swappable” protects you when one provider pulls a model. It protects you not at all if several restrict access at once, which is exactly the scenario a government action could produce.
  • The cost is hidden, and cost is the whole game. Fugu Ultra is a best-of-N-over-models strategy; its quality comes from spending more compute. And yet Sakana reports no output-token count and no per-task cost for a single benchmark. That omission is the tell. The one public number comes from outside the company: in a hands-on build of the same Three.js game, one tester clocked Fugu Ultra at about 89,000 tokens, $7.32, and 22 minutes, against Claude Opus 4.8 in its multi-step “ultracode” mode at about 940,000 tokens, $37.85, and 79 minutes. Fugu came out cheaper and faster; Opus produced the better game. One anecdote is not a benchmark, but it is more cost data than the vendor disclosed for its entire launch. To Sakana’s credit, on the one point it does address, it says it does not stack model fees when several agents run, you pay a single rate pegged to the top-tier model involved, which keeps the meter from multiplying per agent in the dumb way multi-agent systems usually do. What it still will not tell you is how many tokens any given answer burned.
  • It is opaque by design. Fugu does not tell you which model produced which output. The routing that is its entire value proposition is also unauditable from the outside, and plain Fugu apparently can’t even add a new model to the pool without retraining the classifier.

And there is the part that cuts against the pitch. Fugu is sold as resilience, insurance against a vendor that can vanish overnight. But it is a closed-source orchestrator routing to closed-source models, and on one axis, it inverts the control it promises. Before, you did not own the model. Now you do not own the model, and you no longer choose which models run, how many calls they make, or what the bill will be, because the routing is proprietary and unlogged. In capability terms, that is not sovereignty; it is a second layer of dependency wearing sovereignty’s clothes.

Why is a router hard to ban

Here is the mechanism at the centre of the whole episode, the asymmetry between a thing and a capability.

An export control needs a defined object. A chip with a classification number. A weights file above a compute threshold. The June 12 directive showed that a live API can be added to that list. But Fugu is a different kind of object. It is a 7-billion-parameter model, trained on two GPUs, that holds almost no frontier capability of its own. Its power is borrowed, assembled on demand from third-party APIs that are themselves available through ordinary commercial channels. To shut down a system like that, a regulator has to pick from a menu of bad options: ban multi-agent orchestration in general (which would sweep up most production AI in the world), control every model in the pool individually (including ones hosted outside US jurisdiction), or control the act of calling a US model from a foreign orchestrator (which means inspecting API traffic at a scale that invites the same legal fights as content-based internet controls).

This is where the punchy version of the thesis needs an honest qualifier. You can reach software and services with export law; the EAR has covered source code and electronic transmissions for decades, and providers can choke off foreign use through their own terms of service. The claim is not that a router is uncontrollable. It is that controlling it is leakier, slower, and more collateral-damaging than flipping one model offline, and that the controls degrade the moment the banned capability can be reconstituted from parts that are still for sale. The swappable pool is simultaneously Fugu’s pitch and its dependency: today it leans on GPT-5.5, Opus 4.8, and Gemini 3.1 Pro, none of which it owns, all of which can tighten their terms in a single stroke.

The precedent that says this fails: the crypto wars

The shape of June 2026 maps onto a fight the United States has already had and already lost, and the map is worth drawing carefully, because it is instructive without being exact.

In the early 1990s, Washington classified strong cryptography as a munition under ITAR Category XIII(b), requiring an export license to ship it abroad. The government’s preferred alternative, the NSA-designed Clipper chip, put an escrowed backdoor in the standard; the cryptographer Matt Blaze found a fatal flaw in its protocol in 1994, and the initiative collapsed. Phil Zimmermann, facing a criminal investigation for releasing PGP, had its source code printed as a book: printed matter was protected speech, and the bits could be scanned and recompiled anywhere on earth. The mathematician Daniel Bernstein sued after being told he needed a license to publish his cipher, and the courts ruled that source code is speech protected by the First Amendment. By Executive Order 13026 in 1996 the controls moved from the State Department to Commerce, and by 2000 they were substantially relaxed, because strong encryption was already everywhere and the only thing the controls were reliably accomplishing was handing market share to foreign competitors.

 

The differences are real, and you should not pretend otherwise. Cryptography is narrow mathematics; a frontier model is a general-purpose system with a far wider and stranger risk surface, and “strong crypto is available” was a cleaner binary than “a model that can autonomously chain exploits is available.” Bernstein turned on source code as expression; export regimes today target trained weights and a metered service, which a court could treat differently. The analogy is partial, not a proof. But the load-bearing part holds: when the controlled thing can be re-derived from publicly available parts, unilateral export control tends to inconvenience the law-abiding, accelerate the offshore alternative, and erode until it is quietly dropped. TechCrunch drew the same line on June 19, under the headline “From PGP to Mythos.”

The policy fork: block, or race

Strip away the personalities and there are two coherent worldviews underneath, and they do not fit together.

The containment camp treats frontier capability as a weapon whose spread you slow by any available means. Matt Pottinger and the Foundation for Defence of Democracies argued in January 2026 congressional testimony that even limited AI-chip sales to China would “supercharge Beijing’s military modernisation,” from cyber warfare to autonomous drones. Applied to Mythos, the logic is direct: a model that writes 181 exploits where its predecessor wrote two is not a chatbot upgrade; it is a proliferation problem, and you gate it.

The race camp treats restriction as self-defeating. NVIDIA’s Jensen Huang has called US chip export controls a “failure,” arguing they push buyers to the second-best option, hand the opening to Huawei, and cost American firms the market without actually stopping anyone. Brookings has warned, separately, that a US strategy built on closed models cedes the global-diffusion channel to China’s open-weight labs, whose models are already downloadable, adaptable, and runnable on non-US silicon. Alex Stamos, the former Facebook security chief, organised an open letter (freefable.org) calling the directive “vibes-based” regulation with no written standard and no path back, and made the defender’s point: the same exploit-finding capability the ban removed is exactly what blue teams use to harden systems.

The administration itself does not sit cleanly in either camp. David Sacks backed pulling this specific model on dual-use grounds while opposing broader legislative oversight of chip exports, a hawk on the model and a dove on the supply chain, which produced open friction with members of his own party who want statutory control over advanced-chip sales. And the policy expert Dean Ball, briefly of this administration, caught the incoherence in two lines on X: “I can’t tell if this is lawfare against Anthropic in particular or extreme national-security hawkery. Regardless, it is simply cartoonish.” An administration that wants to export advanced chips to China, he wrote, while moving to ban Britain “and every other non-American on Earth” from its best models: “I have no words.”

The allies noticed. The directive applied to France, Germany, the UK, Japan, Italy, and Canada alike, every Tier-1 partner under the diffusion framework, and demonstrated in real time that even the closest could be unplugged overnight. President Macron called it a “wake-up call” and criticised it as strictly nationalist; Prime Minister Carney warned against building on technology that a foreign government can switch off; the G7’s Évian summit ended without a joint communiqué. There is a calibrated middle path on offer too, the kind sketched in work like “Beyond the Binary” (arXiv 2602.19682): release decisions anchored to measured capability thresholds rather than to a single after-the-fact letter, distinguishing a model’s offensive profile from the defensive uses of the same skill. It requires a written standard, which is precisely what June 12 lacked.

And then there is the irony the whole episode turns on. Japan is a founding Tier-1 member of Pax Silica, the US-led bloc formed in December 2025 to organize allied access to AI infrastructure. Tokyo joined the alliance for unrestricted access to the frontier. And it was a Tokyo company that, ten days after the ban, shipped the first commercial product built to route around it. Tier-1 membership buys the chips. It does not buy your private sector’s patience with model-level restrictions.

Sakana is built to be exactly that private sector. Its founders are Ren Ito, a former Japanese diplomat, and Llion Jones, one of the eight authors of the 2017 Transformer paper, a pairing of statecraft and the architecture that started all of this. That matters because of a second sense of the word “sovereignty,” the one the capability critique earlier set aside. Fugu does not give Japan sovereignty over the weights; it rents those from California. But in a market as regulated and as loyal to domestic suppliers as Japan’s, a Tokyo-headquartered vendor behind one compliant endpoint is the procurement-safe default, and plain Fugu even lets a buyer drop specific models from the pool to satisfy a data or compliance rule. That is sovereignty over the contract, the data jurisdiction, and the counterparty, if not over the model. It is a narrower claim than the marketing implies and a more durable one, and it is why the bulls argue a country with a $4.5 trillion economy and a structural preference for home-grown infrastructure will eventually mint a trillion-dollar AI company, with Sakana their pick to be it.

The honest version

The case for blocking is not empty. Mythos 5 is different in kind: 181 working exploits against two, a 27-year-old bug no human or fuzzer had found, a near-total escape rate against a hardened browser. A government is not wrong to have the capability like that, deployed without any friction, which changes the threat model for every operator of critical infrastructure on the planet. Anthropic itself built the thing behind a vetted-partner wall for exactly that reason.

The case for racing is not empty either, and history is on its side. The Clipper chip failed. PGP shipped as a paperback. Bernstein established that code is speech. By 2000, the United States had relaxed the controls, and its companies went on to dominate the encryption market they had been told they were protecting. Today, GLM 5.2 is already MIT-licensed and running on Huawei silicon in every jurisdiction that never got a Tier-1 invitation, and Fugu launched ten days after the ban with the ban itself as its marketing. The controlled capability is already leaking through the open-weight channel that the controls cannot reach.

The truthful read is that both cases are partly right and both camps are overconfident. Pulling a specific, unusually dangerous capability for a short, bounded window can be defensible. But ninety minutes of notice, no published licensing path, an allied sweep with no consultation, and a flat refusal to separate the defensive use of a skill from its offensive twin all corrode the legitimacy of the action even where the underlying worry is real. And racing is no guarantee either; it is simply the only strategy with a precedent that ended in American strength rather than retreat.

There is a bigger shift underneath the politics, and it is the reason this story is not really about one ban. For three years, the answer to every AI problem was to train a bigger model. Fugu is a bet on the next answer: coordinate the models you already have. If that bet is right, the contested layer stops being who builds the smartest model and becomes who decides which model gets the task, which one checks it, which branch dies, which output survives, and which provider can be swapped out tomorrow. The model race does not end. It gets a manager. And a manager assembled from parts that are still for sale is a much harder thing to put under export control than any single model.

The model went dark in an hour. The router shipped in ten days. The open weights are already on Huawei chips. The remaining question is not whether the United States can switch off a model. June 12 settled that. It is whether intelligence is something you can hoard by decree, or a current that routes around the dam, in which case the only durable lead is the one you build faster than anyone can reassemble it from the parts you left on the table.

Happy Coding ❤

Source

   🙏 Donations Accepted, Thank You For Your Support 🙏

If you find value in my content, consider showing your support via:

💳 Stripe:
1) or visit http://thedinarian.locals.com/donate

💳 PayPal: 
2) Simply scan the QR code below 📲 or Click Here

🔗 Crypto Donations Graciously Accepted👇


XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
See More
Available on mobile and TV devices
google store google store app store app store
google store google store app tv store app tv store amazon store amazon store roku store roku store
Powered by Locals