COLUMN: Alibaba Launches HappyHorse 1.1 — China Rushes Into the Void Left by the US

COLUMN: Alibaba Launches HappyHorse 1.1 — China Rushes Into the Void Left by the US

Introduction: Washington Bans, Beijing Prospers

June 13, 2026: A 90-Minute Decision That Changes Everything

On June 13, 2026, the American government gave Anthropic 90 minutes to block access to its Claude Fable 5 and Claude Mythos 5 models for non-American users. 90 minutes. Not 90 days. Not even 90 hours. Anthropic complied — geofencing its most powerful models to American territory in under an hour and a half. This is, in the history of commercial artificial intelligence regulation, an unprecedented act: the application of export control legislation (Export Administration Regulations) to a commercial software service, with a temporal brutality that has no parallel.

The consequences were immediate. Thousands of developers, companies and users around the world — in Europe, across Asia, throughout Latin America, across the Middle East — found themselves cut off from their working tools overnight. Without a prepared substitute. Without warning. And into that void suddenly created by American policy, one actor was waiting, ready to move: Alibaba. Its HappyHorse model, unknown to the broader public just weeks earlier, was about to make a dazzling leap up the global AI rankings. This was not luck. This was strategy.

The Geopolitics of Artificial Intelligence: The New Battleground

We are living through the first great geopolitical battle of the artificial intelligence era. And like all geopolitical battles, it is not fought solely on the technology battlefield — it is fought on the fields of regulation, trade, diplomacy and trust. The American decision to ban the export of Anthropic's models fits within a broader policy of restricting advanced AI technologies to non-allied countries — a policy inspired by the semiconductor export controls targeting China since 2022.

The intention is understandable. Preventing potential adversaries from using the most advanced cognitive capabilities of American models for military, surveillance or disinformation applications is a legitimate national security objective. But the execution raises a fundamental problem: in the domain of AI software, unlike physical chips, open-source alternatives and models developed outside the United States are advancing at a speed that makes partial restriction of access to American models less a security lock than a gift handed directly to the competition.

HappyHorse: Anatomy of a Model That Comes From Nowhere

15 Billion Parameters and a Unified Architecture

The HappyHorse-1.0 model, developed by Alibaba's Taotian Future Life Lab, did not appear with the American ban. It had been introduced in April 2026, passing relatively unnoticed at the time in the constant stream of AI model announcements. Its technical specifications were impressive — 15 billion parameters, a unified 40-layer Transformer architecture, designed for multimodal processing: text, image and video in a single coherent model. But what truly propelled HappyHorse onto the global stage was its performance on public benchmarks.

On the Artificial Analysis Video Arena — one of the most respected community evaluations in AI for video capabilities — HappyHorse-1.0 had reached first place in April 2026 with an ELO of 1383 for text-to-video and 1413 for image-to-video. These scores placed the Chinese model above all its American competitors in this specific segment. This was not marginal. It was first place. And the developer community, which uses these arenas as an objective reference, took note.

The Rise on HuggingFace After the American Ban

Before June 13, 2026, HappyHorse was a respected but non-dominant model on the HuggingFace platform — the global reference for the open-source AI community. Within 72 hours of the American ban on Fable 5 and Mythos 5, downloads of HappyHorse exploded. European, Asian and Latin American developers, searching for an available and powerful substitute, turned en masse to Alibaba's model. Within days, HappyHorse climbed to second place globally on HuggingFace.

This meteoric rise is not merely a popularity indicator — it is a structural shift in the global AI ecosystem. Developers building applications on the foundation of HappyHorse are not just temporary users. They write code, create integrations, build teams around this model. Switching base models six months later will be costly. Adoption creates loyalty. And that loyalty, once acquired at significant international scale, is hard to dislodge. The United States has inadvertently accelerated the global adoption of a Chinese model.

The Adversarial Distillation Affair: 25,000 Fake Accounts

28.8 Million Requests and a Methodical Extraction

But there is a darker layer in the HappyHorse story. According to information published by several specialist sources, Alibaba allegedly conducted a large-scale adversarial distillation operation against Anthropic's models. The method: creating tens of thousands of fictitious accounts on Anthropic's platforms to generate millions of requests and use the responses as training data for its own model. Available estimates speak of 25,000 fake accounts and 28.8 million requests — a volume that far exceeds standard detection thresholds and suggests an organized and deliberate operation.

This practice — if confirmed — raises serious legal and ethical questions. Anthropic's terms of service explicitly prohibit large-scale data extraction and training competing models on the basis of their responses. Several intellectual property law experts have noted that this practice could constitute a violation of contractual conditions and potentially a form of unfair competition. But jurisprudence in this area remains embryonic — courts have not yet established solid precedents on the question of large-scale AI model distillation.

Model Distillation: Common Practice, Blurry Boundaries

Model distillation — using the outputs of a powerful model to train a smaller or different model — is a widely used technique in the AI research community. Many open-source models acknowledge having used data generated by OpenAI models or other major players in their training processes. The boundary between legitimate research and unfair commercial exploitation is, under current law, deeply unclear.

What distinguishes the alleged Alibaba case is the industrial scale of the operation and the use of fake accounts to circumvent detection measures. This is not a researcher using a few thousand requests for an academic study. It is, according to the allegations, an organized operation with considerable resources to methodically extract the knowledge of a competing model. If these allegations prove founded, they raise a problem that goes beyond commercial law — they raise the question of reciprocity in the global AI ecosystem and the ability of Western players to protect their investments in fundamental research.

The Boomerang Effect of American Export Policy

A "Gift to China" According to the Financial Times

The phrasing appeared in the Financial Times on June 15, 2026, two days after the ban: "US export ban is a gift to China." This is not the framing of a partisan commentator — it is the cold assessment of a conservative financial newspaper read by decision-makers worldwide. The reasoning is clear: by cutting access to the most advanced American models for non-American users, Washington created a need that non-American models — primarily Chinese ones — are particularly well positioned to fill.

Analysis of the situation reveals a deep strategic paradox. Export control policies on semiconductors — Nvidia, AMD, Intel chips — carry a certain logic: physical chips are difficult to duplicate, and restrictions create real bottlenecks in computing capacity. But AI models are software. And in the software domain, the open-source paradigm has fundamentally changed the game: when a powerful model is available open source, it can be studied, reproduced and improved by thousands of independent researchers everywhere in the world.

DeepSeek and the Chinese Open-Source Ecosystem: The Hydra That Restrictions Feed

China has developed, despite American restrictions on semiconductors, a robust ecosystem of open-source AI models. DeepSeek V4 reached performance comparable to Claude Opus 4.6 according to evaluations published by Fortune in June 2026. Qwen, Alibaba's model, has repeatedly demonstrated competitive capabilities with the best American models on specific tasks. And now HappyHorse is taking the lead on video benchmarks.

The Chinese open-source ecosystem is not a marginal phenomenon. It is a nationally supported effort backed by massive investment, a world-class research community — many of whom were trained at American universities before being turned away by the restrictive visa policies of the Trump era — and a regulatory framework that, for all its considerable failings on civil liberties, favors massive industrial investment in technologies deemed strategic. Restricting access to American models in this context does not block China. It hands it a market.

Betrayed Allies: Europe and Asia in Anger

Partners Given No Notice, No Alternative

The American government's decision to cut access to Anthropic's models in 90 minutes did not only affect individual users. It hit European, Asian and Latin American companies full force — companies that had built entire products, services and processes on the foundation of these models. Startups that had raised funding on the strength of Claude Fable 5's capabilities. Multinationals whose development teams were in the middle of critical implementations. Academic institutions in the midst of ongoing research.

Al Jazeera reported that the decision had "further strained" American alliances in Asia and Europe. European diplomats expressed their consternation privately, pointing out that their companies were being treated as potential adversaries when they are NATO allies and first-tier commercial partners. This brutality — 90 minutes to cut access for major economic partners — generated a shockwave across already-fragile transatlantic technological relations, relationships already weakened by the trade tensions of the Trump era.

The Paradox of Technological Trust

American export control policy on AI rests on an implicit assumption: that allies and partners of the United States are potential vectors for transferring technology to adversaries. This is an assumption of generalized mistrust that, applied without nuance, transforms partners into suspects. Is a French company using Claude Fable 5 a risk to American national security? Is a Japanese startup building translation tools on Mythos 5 threatening American interests?

The implicit answer of American policy is: possibly. That "possibly" is exactly what fuels its partners' distrust. And that distrust has practical consequences: European and Asian companies that had bet on American models are actively beginning to plan diversification strategies toward non-American models. Not out of anti-American ideology — but out of basic commercial prudence. No one can afford to see their technological infrastructure cut in 90 minutes by the unilateral decision of a foreign government.

The Technology Race From a Different Angle

What Does Leading in AI Video Actually Mean?

HappyHorse's dominance on video benchmarks deserves careful contextualization. AI benchmark rankings are snapshots — they measure performance on specific tasks at a given moment, and may not reflect the model's actual value for concrete applications. A model can rank first on a benchmark and perform poorly for particular professional use cases. The AI research community knows that benchmarks do not tell the whole story.

That said, an ELO of 1413 in image-to-video is not a statistical detail. It is a measure assessed through comparative human votes — thousands of people preferred HappyHorse's video outputs to those of its competitors. In a domain where visual quality and temporal coherence are measurable by any user who watches a video, this type of benchmark has real value. Creative applications — advertising, entertainment, media — are directly affected. And in these sectors, HappyHorse's dominance could translate into concrete market share.

The Video Content Industry: A Massive Market at Stake

AI-generated video is one of the fastest-growing markets in the technology sector. Market studies project that the global market for AI-generated video could exceed 1.2 trillion dollars by 2030. The applications are multiple: personalized advertising, large-scale content production, entertainment, training, design prototyping. Being in the lead on this segment at this precise stage of market development is an extraordinarily valuable strategic position.

Hollywood studios, global advertising agencies, streaming platforms — all are integrating video generation tools into their workflows. The decisions being made today about which model to use will have consequences lasting years. If HappyHorse and its successors establish themselves as the benchmark for high-end video generation in these industries, Alibaba will have achieved a first-magnitude commercial coup — directly facilitated by the American export policy that eliminated its competitors from the global market.

Alibaba's Strategy: Opportunism or Long-Term Vision?

The Taotian Future Life Lab: A Serious Research Infrastructure

The Taotian Future Life Lab, the Alibaba entity that developed HappyHorse, is not a structure assembled in a hurry to capitalize on the American ban. It is a fundamental research organization that has been working for several years on multimodal architectures. The choice of a unified 40-layer Transformer architecture to simultaneously process text, image and video reflects a coherent architectural strategy — building a single foundation model capable of handling different data types with a single latent representation, rather than specialized modules for each content type.

This architectural approach has real advantages in terms of computational efficiency and generalization. It is also, strategically, a response to the dominant paradigm of American models, which have tended to develop separate models for text, image and video. If multimodal unification genuinely produces better overall performance with fewer resources, Alibaba will have contributed an important architectural advance — independent of the controversies over its data acquisition methods.

Version 1.1 and the Accelerating Development Cycle

The release of HappyHorse 1.1 shortly after the initial version illustrates the development cadence that Alibaba is maintaining on this model. In the AI ecosystem, the speed of iterations is a competitive advantage as important as the quality of the initial model. Bug fixes, performance improvements, safety adjustments — all of this determines the extent to which developers trust a model for production applications. A stable, improved version 1.1 sends a signal to developers: the team is committed, responsive, and the model will be maintained and improved.

The open-source availability strategy is also deliberate. By making HappyHorse freely downloadable on HuggingFace, Alibaba forgoes immediate direct revenue in exchange for massive adoption. The goal is ecosystem dominance — building a developer community around the model, generating external contributions and improvements, and creating the long-term dependency that will then be monetized through cloud services, premium APIs and enterprise services. This is exactly the strategy OpenAI used with its open-source versions — and it works.

Anthropic's Response: Between Compliance and Frustration

A Company Caught Between Two Fires

Anthropic is in an extraordinarily difficult position. Founded on principles of AI safety and alignment, its stated mission is to build AI systems that benefit humanity. This mission is global by definition — it assumes that the benefits of safe AI spread as widely as possible. The government injunction to cut international access to its most advanced models enters directly into tension with this mission. But refusing to comply was, legally, a difficult position to defend.

By complying, Anthropic lost international clients to its competitors. Companies that had built their products on Claude were forced to migrate — and many will not return. Trust is the most precious asset of a technology infrastructure provider, and that trust was damaged — not through Anthropic's fault, but through the American government's decision. This is a form of regulatory collateral damage that American decision-makers should have anticipated but evidently underestimated.

The Industry's Response: Between Mobilization and Resignation

Several major companies in the AI sector — OpenAI, Google DeepMind, and others — expressed privately, according to sources cited by Bloomberg, their concerns about the direction American policy is taking. The shared fear: overly broad restrictions could harm the overall American competitive position more than they protect national security. The open model — contributing to global research, attracting the world's best researchers, benefiting from global adoption — is what has allowed the United States to dominate AI until now. Closing it off risks undermining the foundations of that dominance.

But the political pressure in Washington is moving in the opposite direction. The American Congress is in protection mode, not openness mode. Legislators with only a superficial understanding of AI are making decisions whose consequences will play out over a decade. And in the meantime, the American AI industry is navigating growing regulatory uncertainty that complicates its international relationships, product decisions and global recruitment capabilities.

European Digital Sovereignty: Missed Opportunity or One to Seize?

Europe Between Two Giants: A Space Still to Define

Every time the Sino-American technological rivalry intensifies, the same question arises: where is Europe in all of this? The American ban on Anthropic's models and the rise of HappyHorse are yet another illustration of a structural European dependency on AI models developed outside its borders. Europe consumes AI services produced either in San Francisco or in Hangzhou — rarely in Paris, Berlin or Amsterdam.

The European AI Act — which came into application in 2024 — established a regulatory framework but did not resolve the problem of industrial sovereignty. France has Mistral AI — a French gem whose models are respected in the global community. Germany has world-class AI research laboratories. But no European company yet has the capitalization, data resources and computing power needed to compete head-on with the American and Chinese giants. This gap will not close by itself.

Investment in Sovereign AI: A Strategic Necessity

The American ban on Anthropic should be a wake-up call for European decision-makers. If a unilateral decision by Washington can cut millions of Europeans' access to their AI working tools in 90 minutes, then technological dependency is, like energy dependency, a sovereignty risk. The answer cannot simply be to fall back on Chinese models as a substitute — replacing American dependency with Chinese dependency is not a sovereignty policy.

The answer must be to build a proper European capacity — models trained on data that respects European standards, hosted on European cloud infrastructure, subject to European law, technically capable of competing with the world's best. This is expensive. It takes time. It requires coordinated industrial policies at European scale that community institutions still struggle to deploy at the speed of AI. But the alternative — being a spectator of the Sino-American AI rivalry while alternately depending on both — is an untenable position in the long run.

Implications for Global AI Security

When American National Security Undermines Global AI Safety

American AI export control policy rests on a national security logic that presupposes that keeping the most advanced models out of adversaries' reach will reduce the risks of malicious use. This logic has a certain validity for truly differentiated capabilities — models capable of designing biological or chemical weapons, for example. But for text and video generation models like Fable 5 and Mythos 5, the logic is less clear.

In practice, restricting access pushes developers toward models that are less rigorously evaluated on safety. Anthropic is, in the sector, recognized for its particularly serious investment in AI safety research — it is literally its reason for being. By forcing non-American users to migrate toward HappyHorse or other alternative models, American policy directs them toward systems on which safety research is less advanced and less transparent. This is not an improved safety outcome. It is potentially a degraded safety outcome at global scale.

The Control Dilemma in an Open-Source World

The fundamental problem is that the AI world has evolved irreversibly toward a partially open-source paradigm. When a model is published open source — like Meta's Llama, like Alibaba's HappyHorse, like DeepSeek — its weights are downloadable by anyone in the world. No export policy can contain a model whose parameters are freely available on the internet. The only thing restrictions can contain are proprietary models accessible via API — like those from Anthropic.

This reality creates a painful policy paradox. Restricting American proprietary models — the best documented, the best evaluated in terms of safety, the most transparent in their usage policies — favors the adoption of open-source and non-American models, over which no control lever exists. The policy that claims to control AI risks actually creates, in reality, the conditions for an acceleration of those risks outside the American perimeter. This paradoxical outcome warrants a serious reassessment of the strategy.

Possible Futures: Where Is This Race Headed?

Scenario 1: Global Technological Fragmentation

The most plausible short-term scenario is an accelerated fragmentation of the global AI ecosystem. A splintered AI internet divided into geopolitical blocs: American models for close allies of the United States, Chinese models for Beijing's sphere of influence, European models for those seeking to avoid external dependencies, Indian models for a market of 1.4 billion people that refuses to choose sides. This fragmentation is technically inefficient — it duplicates research investment, creates incompatibilities and slows global innovation.

But it is perhaps politically inevitable. The trust between major powers necessary to maintain a genuinely global AI ecosystem simply does not exist in 2026. And incidents like the American ban on Anthropic accelerate precisely this fragmentation that both parties should theoretically want to avoid. The result will be a world where the interoperability of AI systems becomes a geopolitical challenge as complex as the interoperability of telephone standards was in the twentieth century — but with infinitely higher stakes.

Scenario 2: The Open-Source Race as the New Norm

A second scenario — more optimistic in some respects — is that American restrictions accelerate the adoption of open source as the dominant norm for foundation models. If proprietary American models are only accessible to American users, global developers structurally turn toward open-source models — whether Chinese, European, Indian or otherwise. Open source, by definition, escapes national restrictions: once the weights are published, anyone can download, use and modify them.

In this scenario, Alibaba and its Chinese peers who have massively invested in high-performing open-source models would be the big medium-term winners. The United States would maintain an edge on the most advanced proprietary models — but for an American-only market. American global AI dominance, built on global adoption, would be replaced by a fragmented dominance: technically superior on certain axes but geographically limited. This represents a considerable strategic impoverishment of the United States' global position.

What All of This Really Means

Technology as an Extension of Geopolitics

The story of HappyHorse and the American ban on Anthropic is, at its core, a geopolitical story dressed in technology. It illustrates that in the world of 2026, technology is no longer a sphere separate from geopolitics — it is its natural extension, the new battleground where the century's power balances are decided. AI models are not simply commercial tools. They are infrastructures of cognitive power at national and international scale.

Which language does a model understand best? Which cultural biases does it encode in its responses? What data did it use for training, and with what angles? These questions are not technical — they are political. A world where the dominant AI models are developed in China is a different world from one where they are developed in the United States or in Europe. The values, representations and conceptual frameworks encoded in these models will shape the way billions of people process and interpret information. This is an influence power without precedent in human history.

The West Must Choose: Competition or Retreat

Faced with the rise of HappyHorse, the ascent of DeepSeek, the Chinese open-source ecosystem in full expansion, the West has a fundamental decision to make. It can choose competition — massive investment in AI research and development, openness policies that attract the world's best minds, building robust technological alliances with its partners. Or it can choose defensive retreat — growing restrictions, technological walls, progressive isolation — at the risk of losing not only market share but also the legitimacy of a global technological model.

The West won the Cold War because it demonstrated that its model was open, creative, economically superior to the closed Soviet model. The temptation, faced with China, to reproduce Soviet methods — centralized planning, control of exchanges, closure of technological borders — is understandable but strategically dangerous. The West will win the AI competition through quality, openness and innovation — or it will not win it. There is no third path.

AI Regulation in 2026: When Governance Cannot Keep Up With Innovation

The Regulatory Vacuum as Fertile Ground for Chinese Competition

The Alibaba HappyHorse affair highlights a structural problem of global AI governance: state regulation cannot keep pace with the rhythm of technological innovation. American restrictions on access to advanced AI models were designed to maintain a competitive edge over China. But their practical application creates unanticipated perverse effects: pushing international users toward Chinese alternatives, weakening the position of American developers on global markets, and creating precedents for technological fragmentation that complicate international cooperation on AI standards.

This is not a question of incompetent American regulators. It is that the speed of AI innovation is fundamentally incompatible with traditional legislative and regulatory cycles, which take months or years to produce rules. A restriction adopted on the basis of existing models in January 2026 is potentially obsolete by July 2026, when new models with different capabilities have emerged. Alibaba's HappyHorse was able to appear and capture global market share in the time it took Washington to put its restrictions on competing models in place.

Toward International Coordination on AI Standards

The solution to the AI governance problem is not national — it is international. Minimum technical standards on model safety, training data transparency, documentation of capabilities and limitations, and audit and traceability obligations, applied consistently by the world's leading economies, would create a more equitable and safer playing field than current unilateral restrictions. Organizations like the OECD, ISO and ITU are working on these standards, but their progress is too slow relative to the speed of model deployment.

The Seoul Declaration on AI Safety of 2024, signed by around twenty countries, is a beginning. It should be transformed into binding obligations with verification mechanisms. France, which chairs the AI Action Summit for 2025-2026, has attempted to accelerate this process. But without American and Chinese engagement in a common framework — engagement that is difficult to obtain in the current geopolitical climate — the standards will remain statements of good intentions rather than real constraints. AI governance is the test of democracies' capacity to cooperate even when their interests diverge.

AI and Economic Competition: Who Will Capture the Value?

The AI Value Chain: An Unequal Distribution

The rise of models like Alibaba's HappyHorse raises a fundamental economic question: who captures the value generated by artificial intelligence? The AI value chain is complex, distributed across several actors: providers of computing power (NVIDIA and its competitors), developers of foundation models (OpenAI, Anthropic, Alibaba, Google), application developers who use these models, and end users who benefit from productivity gains. In this chain, value tends to concentrate at the extremities: compute providers and model developers capture substantial rents, while applications and end users capture primarily efficiency gains.

The rise of Alibaba as a provider of competitive foundation models — now globally accessible via HappyHorse — shifts part of this rent toward China. For countries that have not developed their own foundation models, dependency on American or Chinese providers creates a strategic vulnerability similar to energy dependency. Europe, which has not yet produced a globally competitive foundation model, faces this dilemma: depend on Washington (with the risk of geopolitical restrictions) or on Beijing (with different security risks). Digital sovereignty requires European foundation models — and their funding remains insufficient.

Global South Countries in the AI Race

The competition between American and Chinese AI models for global markets has a dimension often overlooked: countries of the Global South — India, Brazil, sub-Saharan Africa, Southeast Asia — are the terrain of this competition, but also the potential beneficiaries of democratized access to AI technologies. Alibaba's HappyHorse, being accessible where American restrictions apply, offers emerging markets generative AI capabilities they would not otherwise have had access to. This is a consideration that Western restriction policies must factor in: in their battle against Chinese technological dominance, they risk alienating Southern countries who will see American restrictions as another episode of techno-colonialism.

The optimal strategy for the West is therefore not to restrict access to AI models in emerging markets — it is to offer better models, more accessible, better adapted to local languages and contexts, with transparent terms of use and data sovereignty guarantees. Winning the AI race in the Global South is achieved through quality and accessibility, not restrictions. This is a lesson that Washington still seems to struggle to integrate into its technology strategy.

Conclusion: The Void Washington Created Now Belongs to Beijing

The Lesson That 90 Minutes Taught Us

In 90 minutes, the American government created a void in global access to advanced AI models. Within a few weeks, that void was partially filled by a model developed by a company whose government maintains close ties with the Chinese state. The symmetry is perfect — and damning. The policy designed to protect American technological advantage accelerated the global adoption of a Chinese competitor. This is a strategic failure that deserves to be named clearly, without euphemism.

The HappyHorse case is not an anomaly. It is a preview of what policies of AI export controls will systematically produce when they fail to distinguish partners from adversaries, when they treat European and Asian allies as vectors of risk, and when they forget that technological dominance is built on global adoption, not selective exclusion. If the policy does not change, the next HappyHorse models will bear different names, but the dynamic will be the same: American vacuum, Chinese gain.

A Column Is Not a Prediction — It Is a Warning

I do not know how this technological competition between the United States and China will end. I do not know whether HappyHorse will stay at the top of the benchmarks in six months, whether the allegations of adversarial distillation will be confirmed and sanctioned, whether American policies will soften or harden. What I know is that decisions made today in government offices in Washington, in research laboratories in Hangzhou and in startups in Paris and Berlin, will shape the global technological landscape for decades to come. And that this landscape will be decisive for the capacity of democracies to maintain their advantage against authoritarian systems that, for their part, have a very clear long-term vision.

The lesson of HappyHorse is simple: in the technology war, your adversary's strategic mistake is your best ally. Beijing knows this. It is waiting patiently for Washington's next 90 minutes.

By Maxime Marquette, columnist

Columnist's transparency note

Editorial positioning

This column analyzes the strategic consequences of the American ban on Anthropic's models and the rise of Alibaba's HappyHorse. The opinions expressed are those of the columnist and reflect his geopolitical and technological analysis. They do not constitute a commercial or financial recommendation concerning the companies mentioned. The columnist holds no financial interest in any of the companies cited.

Limitations and uncertainties

The allegations concerning the adversarial distillation of 28.8 million requests and 25,000 fake accounts are reported by specialist sources but have not been officially confirmed by Anthropic or a court. They are presented as allegations, not as established facts. Benchmark data (ELO 1383/1413) comes from the Artificial Analysis Video Arena and reflects a snapshot at the time of publication — rankings in this domain evolve constantly.

Sources

Primary sources

Bloomberg — White House ban on Anthropic AI access may boost China's open-source models — June 26, 2026

Fortune — US Anthropic ban: a boon for open-source AI including DeepSeek — June 16, 2026

The AI Dude — The US export ban on Claude Fable 5 and Mythos 5: explained — June 2026

DataNorth — Alibaba launches HappyHorse, its benchmark-leading video model — June 2026

Secondary sources

Al Jazeera — US export ban on Anthropic's AI models further strains alliances — June 19, 2026

HappyHorse — Official information on the HappyHorse 1.1 model — 2026

Foreign Policy — AI export controls: the American strategy against China — June 2026