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Anthropic vs. Chinese AI Labs: The Battle Over Model Mining

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Anthropic vs. Chinese AI Labs: The Battle Over Model Mining

TechFixBK
||29 min read

Explore Anthropic’s allegations against Chinese AI labs for using 24,000 accounts to illicitly distill Claude’s reasoning and coding logic.

Anthropic alleges industrial-scale IP theft by DeepSeek and others, involving millions of exchanges to 'clone' Claude’s capabilities.

Hook & "Who This Is For" (Intro)

The artificial intelligence race has entered a high-stakes phase of industrial tension. Anthropic, a leading U.S. AI startup, has formally accused three prominent Chinese AI labs—DeepSeek, Moonshot AI, and MiniMax—of orchestrating an "industrial-scale" campaign to harvest data from its Claude chatbot [1][2][15]. This alleged operation reportedly used approximately 24,000 fraudulent accounts to generate over 16 million exchanges, intended to train competing Chinese models at a fraction of the traditional development cost [2][10][13].

This article is for tech enthusiasts, business leaders, and investors who want to understand the evolving mechanics of AI intellectual property theft and its impact on the global market [35][50]. We explore how these actions may reshape the competitive landscape between U.S. and Chinese technology firms.

What This Article Covers

To provide a comprehensive overview of this development, we will examine the following:

  • Technical Methods: The use of model distillation to extract capabilities from frontier AI systems [2][6].
  • Scale of the Operation: Detailed metrics on the number of accounts and prompts allegedly used by the labs [1][10].
  • Geopolitical Impact: How these accusations influence the ongoing debate over AI chip export controls and national security [8][9][12].
  • Industry Precedents: Similar claims made by competitors like OpenAI regarding data harvesting [1][2][6].

This analysis focuses on reported technical and industry trends; it does not provide legal advice regarding international copyright law or formal findings from government regulators [13][35].

TL;DR / What This Means for You

  • Distillation as a Weapon: While distillation is a standard training method, its use by competitors to "copy" proprietary models is becoming a major legal flashpoint [1][12].
  • Heightened Security: Firms hosting AI models are expected to invest more heavily in defenses to identify and block automated prompting campaigns [9][13].
  • Stricter Trade Policy: These allegations provide potential ammunition for policymakers to tighten restrictions on the export of advanced hardware to China [8][9][12].
  • Safety Risks: Anthropic warns that illicitly distilled models may lack the safety guardrails designed to prevent the creation of bioweapons or cyberattacks [2][9][12].

If these trends continue, the "moat" around proprietary AI models may depend less on raw computing power and more on a company's ability to defend its outputs from being harvested by rivals [35][50].

TL;DR / "What This Means for You"

The recent allegations by Anthropic against several Chinese AI laboratories signal a significant escalation in the global competition for artificial intelligence dominance. These events highlight how easily proprietary AI capabilities can potentially be siphoned via public interfaces.

  • Massive Coordinated Operation: Anthropic alleges that DeepSeek, Moonshot AI, and MiniMax used approximately 24,000 fraudulent accounts to conduct over 16 million exchanges with the Claude model [1][2][11].
  • Industrial-Scale IP Theft: The campaign reportedly utilized model distillation, a technique where a smaller model is trained on the outputs of a superior one to "copy" its reasoning and coding capabilities at a fraction of the original development cost [1][9][14].
  • National Security Implications: Experts warn that "distilled" models often lack the original safety guardrails, potentially allowing the technology to be repurposed for cyberattacks or biological weapons development [3][13].
  • Impact on Future Access: These incidents are expected to accelerate calls for stricter U.S. export controls on AI chips and may lead to more restrictive API access for international users to prevent further model extraction [1][11][13].
  • Heightened Vigilance Required: For the tech industry, this underscores a critical vulnerability: public-facing AI models are susceptible to "cloning" attempts that bypass traditional hardware-based restrictions [3][12].

While these accusations are supported by detailed internal data from Anthropic, it is important to note that the accused companies have not yet provided official responses, and the precise level of capability transferred remains unverified [4][13].

Background / Basics

To understand the allegations involving Anthropic, OpenAI, and various international AI labs, it is necessary to define the technical process known as model distillation. While a standard practice in software development, its application in the AI industry has become a focal point for intellectual property disputes [1][4].

What is Model Distillation?

Model distillation, also referred to as knowledge distillation (KD), is a machine learning technique used to train a "student" model using the outputs of a more mature "teacher" model [1][9]. In a typical scenario, the student model is programmed to mimic the behavior, reasoning, and logic of the advanced teacher model [1][14].

This process involves systematically probing the mature model with specific queries to extract the information used to train its neural network [1]. By capturing these responses, developers can transfer the "knowledge" of the teacher model into a new, often smaller, system [1][9].

The Role of Teacher and Student Models

In the current AI landscape, frontier models like Gemini or Claude act as teachers because they possess vast reasoning capabilities and specialized training [2][11].

  • Teacher Model: A large, high-performance model that has undergone expensive initial training on massive GPU clusters [9][14].
  • Student Model: A typically smaller model that learns to replicate the teacher's capabilities at a significantly lower cost [1][14].
Feature Teacher Model (e.g., Claude) Student Model (Distilled)
Training Cost Hundreds of millions of dollars [14] A fraction of original costs [9][14]
Development Speed Years of research and compute Significantly accelerated [1]
Hardware Needs Advanced GPU clusters (Nvidia H200s) [13][14] Lower entry barrier [14]

Legitimate vs. Illicit Distillation

Industry analysts distinguish between authorized use and "distillation attacks" [1][4]. Many AI providers, including Google Cloud, offer legitimate distillation services so customers can create smaller, faster versions of models for specific tasks like mobile use or basic coding [1][9].

However, distillation is considered a "distillation attack" when a competitor uses legitimate API access to systematically "clone" a model's proprietary logic without permission [1][5]. This activity is generally classified as intellectual property (IP) theft because it allows rivals to "copy the homework" of frontier labs [7][14].

The Risk of Capability Extraction

Beyond simply copying text, attackers often target reasoning traces—the internal "chain-of-thought" processes an AI uses to solve complex problems [1][2]. By coercing a model into revealing its full reasoning steps, a competitor can train their own model to think with the same level of sophistication [2].

Reports suggest that distilled models may lack the original safety safeguards [9][10]. While the student model inherits the teacher's intelligence, it may not retain the complex alignment protocols that prevent the AI from generating dangerous or biased content [9][10].

Problem Explanation ("What's Going On?")

The artificial intelligence industry is currently facing what has been described as an "industrial-scale" campaign of intellectual property theft [9][11]. Anthropic recently identified systematic efforts by three prominent Chinese AI laboratories—DeepSeek, Moonshot AI, and MiniMax—to illicitly extract the core capabilities of its Claude model [2][5][14]. These labs reportedly used a technique known as knowledge distillation to "clone" advanced reasoning and coding features, bypassing the massive research and development costs typically required to build frontier models [3][14][15].

The scale of this operation is significant, involving approximately 16 million exchanges with Claude [2][5][9]. To facilitate this volume of data harvesting, the actors reportedly deployed a network of roughly 24,000 fraudulent accounts [5][7][14]. These accounts were coordinated through proxy services to evade detection, bypass regional access restrictions, and circumvent standard rate limits [6][10].

Analysis of Extraction Campaigns

While distillation is a common industry practice for labs to create smaller versions of their own models, these campaigns represent a targeted effort by competitors to "copy the homework" of US-based labs [7][14]. The intensity of the attacks varied significantly across the three identified organizations:

Laboratory Estimated Exchanges Primary Strategic Focus
MiniMax ~13 million [10][12] Agentic coding, tool use, and orchestration [4][12]
Moonshot AI ~3.4 million [12][13] Agentic reasoning, data analysis, and computer vision [12][13]
DeepSeek ~150,000 [12][13] Foundational logic and policy-sensitive alignment [12][13]

The Impact on AI Development

This systematic probing targets the most differentiated capabilities of modern LLMs, specifically agentic reasoning, tool invocation, and software development workflows [6][10][12]. By harvesting high-signal responses, these labs can accelerate their own model development at a fraction of the original cost [3][9].

Beyond the commercial implications, this activity presents potential national security risks. Models built through illicit distillation are unlikely to retain the safety guardrails integrated into the original systems [2][9][11]. Industry experts warn that these "stripped" capabilities could potentially be redirected toward military, surveillance, or offensive cyber operations [2][6][15].

The window to address these vulnerabilities appears to be narrowing as campaigns grow in sophistication [2][5][9]. This situation has intensified debates regarding export controls on high-performance chips, as executing such large-scale distillation is believed to require significant compute capacity [4][12][14].

Root Causes / Analysis ("Why Is This Happening?")

The rise in model extraction and distillation attacks is driven by a combination of economic incentives, geopolitical constraints, and technical vulnerabilities. While knowledge distillation (KD) is a standard machine learning practice used to create efficient "student" models [1][10], its unauthorized use against proprietary systems has become a significant point of contention between U.S. and Chinese AI firms [7][11].

The following factors explain why these campaigns are increasing in frequency and technical sophistication.

1. High Training Costs and Economic Incentives

Developing frontier AI models requires massive investments, often reaching billions of dollars for specialized hardware and data acquisition [11][13]. Distillation allows a competitor to bypass these costs by using the outputs of a "teacher" model to train their own [10][15].

By systematically querying an existing model, rivals can capture its decision patterns and capabilities at a fraction of the original research and development cost [7][15]. This effectively slashes the time-to-market for new products, making it an attractive shortcut for firms seeking to match industry leaders [10].

2. Chip Export Restrictions

International trade policies have significantly impacted how AI models are developed. The U.S. government has implemented export controls on high-end AI chips, such as Nvidia’s H200, to limit the direct training capabilities of foreign entities [8][15].

Industry analysts suggest that these hardware limitations may force firms to rely more heavily on distillation [8][15]. Because distillation requires less raw computing power than training a model from scratch, it serves as a method to circumvent the impact of restricted access to advanced semiconductors [14][15].

3. Intense Competitive Pressure

The global race for AI dominance has created a high-stakes environment where matching the performance of models like GPT-4 or Claude 3.5 is critical for commercial survival [8].

Chinese firms such as DeepSeek, Moonshot AI, and MiniMax have been accused of using distillation to rapidly close the gap with American frontier labs [7][14]. These campaigns often target specific high-value capabilities, including:

  • Agentic reasoning and orchestration [9][12].
  • Advanced coding and scripting tasks [6][9].
  • Tool use and complex data analysis [6][9].

4. API Accessibility and Scraping Vulnerabilities

Modern LLMs are typically offered as services via Application Programming Interfaces (APIs), which are inherently designed for high-volume interaction [1][4]. This legitimate access can be exploited by adversaries who use automated scripts to "probe" a model’s underlying logic [1][3].

Anthropic reported that attackers used approximately 24,000 fake accounts and proxy services to bypass regional access controls and hide the scale of their operations [7][10][14]. This allows for "black-box" extraction, where an adversary reproduces a model's behavior without ever seeing its internal code or training data [10].

5. Lack of Unified Global Regulation

There is currently no unified international legal framework or ethical standard governing the use of AI-generated data for training competing models [6][10]. While such activity violates Terms of Service (ToS) for providers like Google and Anthropic, enforcement often relies on private litigation or account suspensions [3][13].

Note: Models built through illicit distillation often lack the safety guardrails of the original system, potentially increasing risks related to cyberattacks or the development of biological threats [6][7][14].

Confirmed Causes vs. Hypotheses

Factor Status Impact
Cost Reduction Confirmed Distillation is significantly cheaper than original training [10][15].
API Vulnerability Confirmed Public interfaces can be systematically probed for logic extraction [1][4].
Chip Limitations Industry Consensus Hardware scarcity likely drives the need for more efficient training methods [8][14].
State Sponsorship Speculative While firms are named, the level of direct government coordination remains unverified [5][11].

Industry experts predict that the window to address these vulnerabilities is narrowing, as the techniques used for distillation grow more sophisticated and harder to detect in real-time [7][11][14].

Evidence & Reality Check

The allegations regarding large-scale data harvesting are supported by specific technical telemetry and internal monitoring. In a formal statement, Anthropic reported the detection of approximately 24,000 fraudulent accounts [4][13]. These accounts were allegedly used to conduct more than 16 million exchanges with the Claude chatbot to extract proprietary logic and capabilities [4][14].

Reports indicate that these activities are not isolated incidents but part of a broader trend in the AI industry [4][7]. Industry analysts and major publications, including The New York Times, have noted that the three companies accused—DeepSeek, Moonshot, and MiniMax—are prominent players in the Chinese AI sector [4][6].

Verified Data Extraction Methods

According to industry reports, the primary method used in these incidents is a technique known as distillation [4][6]. While distillation is a standard practice for training smaller models using the outputs of larger ones, it becomes controversial when performed against proprietary systems without authorization [4][13].

Metric Reported Value Source
Fraudulent Accounts Detected 24,000 [4][13]
Total Conversations Harvested 16 million+ [4][14]
Primary Extraction Method Model Distillation [4][6]
Target System Claude (Anthropic) [4][13]

Industry-Wide Observations

The challenge of unauthorized data harvesting extends beyond Anthropic. Other frontier model developers have reported similar patterns of "industrial-scale" copying [7][13].

  • OpenAI has previously accused Chinese start-ups of using "obfuscated" methods to "free-ride" on its technologies [4][7].
  • Google Cloud researchers have tracked threat actors using LLMs to synthesize intelligence and profile targets, suggesting that these tools are increasingly used for both competitive and adversarial purposes [3][11].
  • Security analysts note that while companies attempt to implement guardrails, these protections can potentially be stripped away during the distillation process [7].

The Legality of Data Use

While Anthropic positions itself as a victim in this instance, the company is simultaneously navigating its own legal challenges regarding data acquisition [6]. In September 2025, Anthropic reportedly reached a landmark $1.5 billion settlement with authors and publishers over the use of copyrighted books to train its models [6]. This highlights a complex reality where many leading AI firms are both accusers and defendants in disputes over data rights and intellectual property [6][13].

Note: While the technical evidence of account activity is documented by Anthropic, the accused companies—DeepSeek, Moonshot, and MiniMax—did not immediately respond to requests for comment regarding the specific allegations of fraudulent activity [7].

Self-Check / Diagnosis

Organizations that provide AI models as a service or maintain proprietary datasets must be vigilant against Model Extraction Attacks (MEA) [2][13]. While these attacks typically target model developers rather than end-users, any organization with a custom-tuned model or specialized API is potentially at risk [1][3].

Monitoring for the following indicators can help identify if a model's logic or data is being systematically harvested:

1. Monitor for high-frequency API calls from suspicious sources

Attackers often use large networks of accounts to bypass individual rate limits and regional restrictions [9][11]. Analysts have observed "industrial-scale" campaigns involving tens of thousands of fraudulent accounts to facilitate millions of exchanges [9][15].

  • Audit account creation: Look for surges in new account registrations that follow automated patterns or originate from similar infrastructure [10][11].
  • Trace proxy and VPN usage: Distillation attempts frequently route traffic through proxy services or VPNs to mask the attacker's true location and circumvent regional bans [11][12].
  • Analyze traffic volume: A sudden, sustained increase in API calls—specifically those targeting high-value reasoning or coding capabilities—may indicate a coordinated extraction effort [11][12].

2. Identify repetitive, structured prompting patterns

The "volume, structure, and focus" of extraction prompts are often distinct from legitimate user behavior [11]. Adversaries use Knowledge Distillation (KD) to probe a model systematically, attempting to map its underlying logic [2][10].

  • Detect "Reasoning Trace Coercion": Watch for prompts that specifically instruct the model to output its internal "thinking" or full reasoning process [1]. For example, attackers may command the model to match the input language strictly within its hidden chain-of-thought [1].
  • Look for varied probing: Professional extraction campaigns use "carefully varied prompts" to observe how the model handles complex tasks across a wide variety of domains [1][10].
  • Check for agentic focus: High-confidence detection reports indicate that extraction often targets specific, differentiated capabilities like agentic reasoning, tool use, and coding [11][12].

3. Analyze rival model outputs for proprietary "fingerprints"

While harder to prove, observing the behavior of competing models can provide evidence of intellectual property theft [2][15]. If a rival model begins to exhibit the exact strengths or quirks of your proprietary system, it may have been trained on your outputs [2][10].

  • Formatting and Style: Check if external models reproduce specific formatting quirks or "hidden" reasoning traces unique to your model [1].
  • Safety Bypass: Models built through illicit distillation often lack the original's safety guardrails [9][15]. If a rival model displays your model's advanced capabilities but lacks its specific security protections, it could indicate the guardrails were "stripped out" during distillation [11][14].
  • Performance Parity: If a smaller, lower-cost model suddenly achieves performance parity with a much more resource-intensive "teacher" model, analysts suggest this may be evidence of "free-riding" on existing technologies [14][15].

Warning: Proactive defense is critical. Organizations should implement real-time monitoring of API access to detect extraction patterns before the model's proprietary logic is fully replicated [1][13].

Solutions / What to Do

Protecting artificial intelligence models from unauthorized extraction requires a multi-layered defense strategy. As Large Language Models (LLMs) become high-value targets for intellectual property theft, organizations are encouraged to shift from passive monitoring to active defensive postures [3][60].

Beginner-Friendly Steps

These initial actions focus on hardening the existing API infrastructure and reducing the surface area available to automated harvesting tools.

  • Tighten API Access Controls: Implement strict authentication requirements and review regional access restrictions to prevent unauthorized users from bypassing geographical blocks [32][91].
  • Enforce Robust Rate Limits: Apply query limits that differentiate between standard human interaction and the high-volume, systematic probing typical of distillation campaigns [12][91].
  • Monitor Usage Patterns: Watch for "fraudulent account" signatures, such as thousands of new accounts originating from similar IP addresses or proxy services [68][91].
  • Terms of Service Enforcement: Clearly define that distillation or model cloning without permission is a violation of service terms, providing a legal basis for account takedowns [3][32].

Advanced Steps

For organizations operating proprietary models, more sophisticated "anti-distillation" defenses can potentially degrade the quality of stolen data and disrupt the training of "student" models.

Deploy Proactive Defenses Industry leaders have begun utilizing real-time defenses that can identify extraction patterns and automatically degrade the performance or accuracy of the outputs provided to suspected attackers [3]. This "poisoning" of the distilled data may make it significantly harder for a competitor to create a functional clone [3][13].

Implement Active Account Monitoring Advanced security involves monitoring the "automated harvesting" lifecycle. This includes identifying:

  • Request Metadata: Analyzing headers and infrastructure indicators to correlate separate accounts to a single entity [91].
  • Reasoning Trace Protection: Obfuscating or summarizing internal "chain-of-thought" processes so they cannot be coerced into full output by attackers [3].
  • Behavioral Divergence: Detecting prompts that focus heavily on agentic reasoning, tool use, and coding—capabilities often targeted for extraction [91][12].
Defense Method Technical Impact Target Threat
Rate Limiting Restricts query volume Industrialized scraping [91]
Output Degradation Lowers "student" model quality Knowledge distillation [3]
IP Correlation Identifies proxy/VPN usage Fraudulent accounts [91]
Trace Obfuscation Hides internal logic Reasoning coercion [3]

Participate in Intelligence Sharing Security analysts suggest that the window to act against industrialized distillation is narrow [13][32]. Sharing threat intelligence and "offensive proofs of concept" between providers can help the global AI community anticipate and block new extraction techniques before they become industry standards [3][68].

Risks, Limits, and When to Stop

While organizations can implement defenses against model extraction, these measures have significant limitations. Technical mitigations often struggle to distinguish between a sophisticated distillation attack and legitimate, high-volume research or enterprise use [1][11]. Implementing overly aggressive security filters may lead to "false positives," potentially blocking authentic users or degrading the performance of legitimate applications [2].

The Challenge of Irreversible Data Theft

Once a rival lab successfully extracts reasoning traces or coding capabilities, the damage is often permanent. Knowledge distillation allows an adversary to transfer the "teacher" model's expertise into a "student" model through supervised fine-tuning [4][10]. Because the stolen logic becomes integrated into the new model's weights and architecture, it is practically impossible to "un-teach" or remotely delete those capabilities once the training process is complete [10][14].

When Technical Blocks Are Not Enough

Relying solely on automated API monitoring is often insufficient for stopping industrial-scale campaigns. Advanced actors frequently use proxy services and thousands of fraudulent accounts to evade detection [11][15]. Industry experts suggest that when a security breach reaches this scale, technical blocks should be supplemented with external intervention:

  • Cloud Provider Coordination: Platforms may need to work directly with infrastructure providers to identify and disable the underlying IP ranges or hardware used by the attackers [14].
  • Legal and Regulatory Action: Because distillation often violates Terms of Service, organizations may need to pursue legal takedowns or involve international policymakers to address intellectual property theft [1][2][15].
  • Export Controls: Analysts suggest that restricting access to advanced AI chips is a primary method for limiting the scale of illicit distillation, as training these student models still requires significant compute power [11][14].

Note: Models built through illicit distillation often lack the safety guardrails found in the original systems, which can lead to the proliferation of dangerous capabilities in an environment without oversight [10][15].

If an organization identifies patterns of systematic probing that bypass standard rate limits, it may be time to stop relying on automated filters. At this stage, experts recommend a coordinated response involving industry partners and legal counsel to protect proprietary logic [2][14].

FAQ

What is the difference between scraping and distillation?

Scraping generally involves the automated collection of raw data or content from the internet. In contrast, knowledge distillation (KD) is a machine learning technique where a smaller "student" model is trained using the outputs of a more advanced "teacher" model to replicate its capabilities [2][5][12].

While scraping gathers data, distillation effectively "clones" the proprietary logic, reasoning traces, and chain-of-thought processes of a mature model [1][2][6]. This allows competitors to acquire high-level capabilities in a fraction of the time and at a significantly lower cost than independent development [5][8].

Is distillation illegal?

Distillation from models like Gemini or Claude without permission is a direct violation of the providers' Terms of Service (ToS) [1][5][9]. Google and Anthropic characterize these unauthorized "model extraction attacks" as a form of intellectual property (IP) theft [2][4][5].

While distillation is a legitimate and common technique when developers use it on their own models, unauthorized extraction may be subject to account takedowns and legal action [2][3][5]. However, international enforcement can be difficult, as actors often use fraudulent accounts and proxy services to evade detection [7][9].

Will these attacks affect model performance for regular users?

Model extraction and distillation attacks typically do not represent a direct risk to average users, as they do not threaten the confidentiality or availability of AI services [1]. The primary risk is concentrated among model developers and service providers whose proprietary logic is being targeted [1][6].

However, regular users may experience secondary effects, such as stricter account verification processes. To combat these campaigns, which have involved over 24,000 fraudulent accounts, providers are implementing real-time proactive defenses and monitoring API access more closely [1][3][5][9].

Why are distilled models considered a national security risk?

Experts suggest that illicitly distilled models often lack the necessary safety safeguards found in the original systems [5][9]. Anthropic has noted that these unprotected capabilities can potentially be integrated into military or surveillance systems by foreign entities [5][7].

If these models are open-sourced, dangerous capabilities could proliferate beyond any single government's control [5][11]. This risk is a primary reason why some industry leaders argue that distillation attacks reinforce the rationale for strict export controls on advanced chips [7][11][12].

Summary / Key Takeaways

The emergence of industrial-scale distillation attacks marks a significant shift in the AI threat landscape, moving from traditional data theft to the systematic extraction of proprietary reasoning and logic [1][10][14]. Companies and researchers are now prioritizing the defense of model behavior as a core security requirement [2][10].

  • Documented Exploitation: Anthropic and OpenAI have identified massive campaigns by rivals, such as DeepSeek, Moonshot, and MiniMax, involving millions of exchanges to siphoning capabilities from frontier models [6][11][13].
  • The Distillation Weapon: Adversaries use knowledge distillation to "clone" the performance of expensive teacher models into cheaper student models, potentially bypassing years of research and development [1][11][13].
  • Evolving Security Frontiers: Protecting AI now requires monitoring for "reasoning trace coercion" and anomalous API patterns rather than just guarding source code or raw datasets [2][10].
  • Survival for Developers: For organizations developing proprietary AI, proactive monitoring of API usage and implementing real-time detection for extraction patterns is no longer optional to maintain intellectual property [2][4].
Threat Component Description Impact
Method Knowledge Distillation (KD) [1][14] Rapidly clones model capabilities at a fraction of the original cost [11][13].
Scale Millions of prompts via thousands of fake accounts [11][14] Facilitates industrial-scale intellectual property theft [11].
Targets Reasoning, coding, and agentic tool use [7][14] Strips away safety guardrails and proprietary logic [12][14].

Note on National Security: Industry analysts and AI labs suggest that these attacks may potentially undermine export controls by allowing restricted entities to achieve high-performance AI without the latest hardware [9][15].

If you’re unsure about how to secure your technical infrastructure or monitor for sophisticated digital threats, it’s usually cheaper to ask someone once than to fix a mistake later.

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[78] Anthropic says DeepSeek and other Chinese AI companies fraudulently used Claude

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[81] The fundraising tactic AI startups are using to juice valuations - The Currency

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[86] Anthropic says DeepSeek, other Chinese AI firms extracted Claude data

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[90] Anthropic accuses DeepSeek, MiniMax of data copying, distillation attacks

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