Anthropic vs Deep Seek: The Claude Distillation Controversy Explained [2025]

In early 2025, one of the biggest AI industry conflicts came into the open. Anthropic, the company behind Claude, publicly accused three Chinese AI companies of systematically stealing its models. Deep Seek, Mini Max, and Moonshot allegedly created over 24,000 fraudulent accounts and ran more than 16 million queries through Claude specifically to extract its capabilities as reported by Seeking Alpha.

This isn't just corporate drama. The implications reach deep into national security, international AI competition, and how frontier AI models get protected (or don't). If what Anthropic describes is accurate, it reveals a massive vulnerability in how cutting-edge AI systems are secured. If the accusations are overstated, they still expose real tensions in the global AI race.

Here's what actually happened, why it matters, and what comes next.

TL; DR

16 million fraudulent queries: Deep Seek, Mini Max, and Moonshot created 24,000+ fake accounts to extract Claude's capabilities at scale according to UC Strategies.
Model distillation technique: Smaller, cheaper models trained on outputs from larger models (legitimate method, but used here illicitly) as detailed by Anthropic.
Deep Seek's focus: Specifically targeted Claude's reasoning abilities and generated censorship-safe alternatives to politically sensitive questions as noted by Abacus News.
National security concern: Extracted models could be embedded in military, surveillance, and intelligence systems without safety guardrails as discussed by Brookings.
Industry response: Both Open AI and other labs facing similar accusations; calls for chip access restrictions and international oversight as reported by Google Cloud.
The bigger picture: This reveals a fundamental weakness in the current approach to AI security and competitive advantage.

What Is Model Distillation and Why Is It Legal?

Model distillation sounds complicated, but the core idea is straightforward: take a large, powerful AI model, have it answer a bunch of questions, and use those answers to train a smaller, cheaper model that behaves similarly.

It's like studying from lecture notes instead of attending the actual lecture. The notes capture the essential knowledge at a fraction of the time investment.

Industry experts, researchers, and even Microsoft Research and Google Research teams have published papers on distillation for years. It's a legitimate, widely accepted technique that companies use with their own models all the time. Anthropic itself has published research on distillation methods.

The legal problem isn't the technique itself. It's the terms of service violation.

When you access Claude through Claude.ai or via API, you agree to specific terms. Those terms explicitly prohibit using Claude's outputs to train competing AI systems. It's right there in the agreement. You can't use Claude to build a better version of Claude for yourself and sell it.

What makes the Deep Seek situation particularly egregious is the scale and intentionality. We're not talking about one engineer running a few queries on their coffee break. This was an orchestrated campaign involving thousands of fake accounts, millions of queries, and deliberate attempts to extract specific capabilities as noted by Interconnects AI.

Anthropic legitimately warns that even if distillation itself is legal when authorized, it becomes "illicit" when used to bypass terms of service. The distinction matters for understanding why Anthropic took action despite distillation being common in academia and industry.

The Scale of the Extraction Campaign

Let's talk numbers, because the scale here is honestly staggering.

Anthropic details revealed that across three companies, there were 24,000+ fraudulent accounts created specifically to query Claude. The accounts cycled through more than 16 million exchanges total. That's not someone casually testing Claude's output. That's an industrial operation as reported by Anthropic.

Breaking it down by company:

Deep Seek made approximately 150,000 exchanges with Claude. Out of the three, Deep Seek was the most aggressive. They specifically targeted Claude's reasoning capabilities, which is interesting because reasoning is where Claude has genuine competitive advantages. They also used Claude to generate "censorship-safe alternatives" to sensitive questions about dissidents, authoritarianism, and political figures. This detail suggests they weren't just copying Claude's general intelligence—they were trying to understand how Claude handles politically sensitive content to create safer versions for Chinese users as noted by Anthropic.

Mini Max conducted about 13 million exchanges. That's the bulk of the fraudulent traffic. Mini Max appears to have been running broad, systematic extraction across Claude's capabilities.

Moonshot had around 3.4 million exchanges. The smallest of the three in terms of raw volume, but still substantial.

To put this in perspective, that's the equivalent of a single user making 16 million API calls. The compute costs alone would have been substantial if paid at normal rates, but these companies used fraudulent accounts specifically to avoid detection and potentially bypass rate limits as detailed by Anthropic.

What's particularly notable is that these weren't random queries testing Claude's general performance. The extraction was targeted. Deep Seek specifically wanted reasoning capabilities and political content filtering. This suggests they analyzed what Claude does well, then methodically extracted exactly those capabilities.

This level of coordination also suggests organizational approval at some level. You don't accidentally create 24,000 fake accounts and run millions of queries. This required budget allocation, engineering resources, and management sign-off as reported by Anthropic.

How Deep Seek Used Claude's Reasoning

Deep Seek's focus on reasoning capabilities is the most interesting part of Anthropic's accusation.

Reasoning is hard. It's the AI capability that most directly correlates with general intelligence. When you ask an AI model to break down a complex problem, think through multiple approaches, and show its work, that's reasoning. Claude has been explicitly trained to be strong at reasoning through techniques like constitutional AI and reinforcement learning from human feedback as detailed by Anthropic.

Deep Seek apparently wanted that same capability in their own models. So they extracted it.

The way this likely works: Deep Seek queried Claude with complex reasoning problems, captured Claude's detailed responses that showed step-by-step thinking, then trained their own models on those responses. Their models learned the patterns of how to reason by mimicking Claude's reasoning process.

Less publicized but equally important: Deep Seek specifically asked Claude for "censorship-safe alternatives to politically sensitive questions." This is a remarkable detail because it reveals intent beyond pure capability theft. They wanted to understand how Claude navigates restrictions around politically sensitive topics in China. They wanted to know what Claude would say about dissidents, authoritarianism, and party leaders—and more importantly, they wanted to learn how to create versions that wouldn't say those things.

This suggests a secondary goal: building Chinese AI models that are powerful enough to compete globally, but safe enough (from a Chinese government perspective) to deploy domestically. Using Claude as the reference model for what NOT to do on sensitive topics is a clever way to train around restrictions as noted by Anthropic.

The reasoning extraction is also significant because reasoning capabilities are expensive and time-consuming to build from scratch. Open AI spent significant resources building GPT-4's reasoning capabilities. Anthropic invested heavily in Constitutional AI to improve reasoning and alignment. Getting those capabilities for the cost of API queries is an enormous shortcut.

This is why Anthropic specifically highlighted the reasoning extraction: it's not about copying general knowledge, it's about acquiring developed, refined capabilities that took teams months or years to build. The efficiency gains are massive as reported by Anthropic.

The National Security Angle

Anthropic's public statement deliberately emphasizes the national security implications, and this is worth taking seriously regardless of where you stand on tech policy.

Here's their core argument: if Chinese AI companies extract capabilities from American frontier models, they can embed those capabilities into military, intelligence, and surveillance systems. And because the extracted models weren't originally trained with safety guardrails in mind, they would operate without the restrictions that frontier AI labs typically build in as discussed by Brookings.

This creates a scenario where authoritarian governments could deploy frontier-level AI for offensive cyber operations, disinformation campaigns, and mass surveillance—without the safety considerations that American labs typically implement.

Now, let's be honest about what's happening here. This is partly Anthropic making a legitimate security argument, and partly standard industry politics. Whenever there's controversy involving China, national security gets invoked. That doesn't mean the argument is wrong, just that it serves multiple purposes.

But the mechanics of the concern are sound: an unaligned, unconstrained AI system is genuinely more dangerous than one with built-in safeguards. If you remove the safety training that Claude goes through, you don't get a faster Claude—you get something that could be considerably more harmful in hostile hands as noted by Brookings.

Anthropic also notes that "restricted chip access" could help limit model training and prevent illicit distillation at scale. This is code for "maybe governments should restrict access to the semiconductors needed to train large models." It's a policy recommendation dressed in security language as reported by Google Cloud.

The national security argument also gets used as cover for competitive concerns. Open AI made similar accusations against Deep Seek in their own letter to lawmakers. Both companies have obvious business incentives to see Chinese competitors restricted. That doesn't make the security concerns invalid, but it's worth keeping in mind when evaluating the fervor with which these concerns are being raised as noted by Google Cloud.

What's undeniable: if Chinese AI companies are building their own models by extracting from American models, the competitive timeline gets compressed. Instead of 5-10 years of independent development, you get competitive models in months. That's strategically significant whether you care about national security or just about market competition.

What Open AI Said (And Why It Matters)

Anthropic wasn't alone in calling out Deep Seek. Open AI sent its own letter to lawmakers accusing Deep Seek of "ongoing efforts to free-ride on the capabilities developed by Open AI and other U. S. frontier labs."

The wording is careful. "Free-ride" suggests unfair advantage. "Capabilities developed by" emphasizes that this is stolen intellectual property. "Other U. S. frontier labs" frames it as an industry-wide problem, not just a competitive squabble between two companies as noted by Google Cloud.

Open AI's phrasing also positions this as theft from the entire American AI industry, not just themselves. This is clever politics because it makes the issue bigger than one company's competition with another. It becomes an American industry vs. Chinese companies issue.

But here's what's interesting: Open AI doesn't detail specific extraction campaigns against their own models in the way Anthropic does. They're referencing Anthropic's findings and extending them. This suggests either:

Open AI has similar evidence but isn't publicizing it yet
Open AI is amplifying Anthropic's findings for political effect
Both companies are coordinating their messaging

The first option seems most likely. If Deep Seek is extracting from Claude, they're almost certainly extracting from GPT-4 and GPT-4o as well. The techniques would be identical. Open AI simply might not have published the detailed findings yet.

What matters: when two major frontier AI companies make coordinated accusations against competitors using similar language and timing, it signals genuine industry concern. It also signals that they're willing to use public pressure and government intervention as competitive tactics as reported by Google Cloud.

The Bigger Picture: AI Model Theft and Competitive Dynamics

This situation exists within a broader context of how frontier AI models get developed, protected, and competed for globally.

The AI industry operates on a fundamental assumption: models are protected intellectual property that companies control through access restrictions and terms of service. You can use Claude through an API, but you don't own Claude. You can use GPT-4, but Open AI controls it.

This is different from open-source software, where the code itself is shared. Most frontier AI models are closed. You get access, not ownership. This creates an incentive structure where companies race to build better models, then carefully control who can access them and how they can be used as noted by Interconnects AI.

Distillation undermines this entire structure. If anyone can extract capabilities from a closed model, the protection is illusory. The only real protection is speed: if you can build competitive models faster than others can extract from yours, you maintain an advantage. Once someone catches up through extraction, that advantage disappears.

This is why Anthropic is pushing for "restricted chip access." If you restrict the semiconductors available to train large models, you make it harder for anyone (but especially geopolitical rivals) to build competitive models from scratch or extracted data. It's a crude tool, but it's one of the few levers available as reported by Google Cloud.

The distillation issue also highlights why some researchers advocate for open-source AI models. If models are open, companies can't complain about extraction because extraction was always allowed. The cost is that you lose competitive advantages from proprietary models. The benefit is transparency and democratization as noted by Interconnects AI.

Anthropic itself contributes to open-source AI. Their Constitutional AI research is published. But their commercial models remain closed. They want the benefits of both: open research that builds credibility and moves the field forward, plus proprietary models that generate revenue as reported by Anthropic.

Deep Seek's distillation campaign—if accurately described—reveals the tension in this strategy. You can't keep a closed model proprietary if determined competitors can extract its capabilities at scale.

How the Fraud Detection Worked

One question you might have: how did Anthropic detect this? 24,000 fraudulent accounts is a lot, but presumably Anthropic has millions of legitimate users. How did they identify the difference?

The technical details aren't fully public, but we can infer from what Anthropic said publicly:

First, account creation patterns would stand out. Normal users create accounts occasionally. Creating 24,000 accounts in a concentrated time period raises flags. Especially if they're all using similar IP addresses or related email patterns.

Second, query patterns would reveal systematic extraction. Normal users ask diverse questions across different topics and use cases. Systematic extraction involves asking specific types of questions repeatedly in ways designed to extract particular knowledge. The fact that Deep Seek specifically targeted reasoning capabilities would show in analytics—lots of queries about multi-step problems, logical reasoning, structured thinking.

Third, response harvesting would be visible if they automated it. If someone is running a script to query Claude, capture responses, and store them at high volume, that would create traffic patterns distinct from normal usage. Rate limiting and behavioral analysis could flag this.

Fourth, account characteristics would be suspicious. New accounts with immediate heavy usage, no typical user behavior, accounts that might share infrastructure. Services like Google's fraud detection teams have sophisticated tools for identifying coordinated inauthentic behavior. Anthropic likely uses similar technology.

Fifth, geographic and temporal patterns would cluster. If 24,000 accounts are created in a specific region and operate at times aligned with business hours there, that's a massive red flag.

Anthropic would also look at device fingerprinting, behavioral biometrics, and other signals that indicate machine-generated vs. human activity as detailed by Anthropic.

That's how you detect systematic fraud at scale: you look for patterns that deviate from normal user behavior. Random individual users don't trigger these patterns. Organized, automated extraction campaigns do.

Anthropic's ability to detect this also suggests they're running sophisticated monitoring. Any company operating a frontier AI model needs to. The stakes are too high not to as reported by Anthropic.

The Technical Challenge: Why Distillation Works

You might wonder: why does distillation work at all? Couldn't Anthropic just obfuscate Claude's outputs to make distillation ineffective?

The answer is more nuanced than you might think.

When Claude produces an output, it's generating human-intelligible text. That's a fundamental requirement of the system. You can't make Claude's outputs less intelligible without making the system useless to legitimate users.

Distillation works because it doesn't require perfect copying. If you extract 80% of Claude's reasoning capabilities, you've dramatically accelerated your own model development. You don't need to get 100%. You need to get enough to be competitive as noted by Anthropic.

There are some technical defenses that could help:

Watermarking: You could embed hidden signals in outputs that don't affect human readability but make it harder for models to learn from them. Anthropic has published research on watermarking. But watermarking is an arms race. For every watermarking scheme, there are potential circumvention techniques.

Output degradation: You could deliberately reduce the quality of outputs provided via API compared to internal use. But this penalizes legitimate users and is hard to maintain at scale. Someone will notice the quality difference.

Rate limiting: Restrict how many queries a user can make. But this limits legitimate high-volume use cases and can be circumvented with more accounts.

Behavioral restrictions: Prohibit queries that look designed for distillation. But this requires perfect detection and can incorrectly flag legitimate use cases.

The reality is that there's no perfect technical defense against distillation if someone is willing to invest resources. The best defense is a combination of:

Detection and enforcement (what Anthropic did)
Terms of service with clear prohibitions
Legal action when violations occur
Regulatory pressure for broader industry standards
Speed advantage from building better models faster than they can be extracted

Anthropic is using all five of these approaches. They detected the fraud, they're citing terms of service violations, they're pursuing legal/regulatory action, and they're presumably working on improving Claude faster than competitors can extract its capabilities as reported by Anthropic.

What This Means for the AI Industry

This situation has several immediate implications for how the AI industry operates going forward.

First, security becomes central to competitive strategy. Companies will invest more heavily in fraud detection, account security, and anomaly detection. API services will become more expensive to operate because of security overhead. This increases barriers to entry for new AI companies that don't have resources for sophisticated monitoring.

Second, terms of service will get stricter. Already, most AI providers prohibit using their outputs to train competing models. Expect these terms to get more aggressive, with harsher penalties for violations. You might see clauses that allow companies to ban users at will, restrict commercial use, or require users to explicitly declare their intended use.

Third, regulatory scrutiny will increase. When competing companies accuse each other of theft in open letters to lawmakers, politicians pay attention. Expect more government interest in:

How AI models are protected
What constitutes fair use vs. theft
Export controls on AI models and training data
International agreements on AI development

The U. S. is already restricting chip exports to China. This situation will likely accelerate those restrictions and potentially extend them to model access as noted by Google Cloud.

Fourth, the open-source vs. proprietary debate will intensify. If closed models can be extracted at scale, that undermines the competitive advantage argument for keeping models closed. Some companies might shift to open-source to avoid the appearance of trying to lock people out. Others will double down on proprietary models and security as noted by Interconnects AI.

Fifth, the geopolitical dimension becomes unavoidable. This isn't just Anthropic vs. Deep Seek. It's implicitly about American technological leadership vs. Chinese technological development. That framing attracts government interest and funding, which accelerates the competition but also creates nationalist and protectionist dynamics as discussed by Brookings.

Deep Seek's Response and Perspective

As of the time this situation became public, Deep Seek hadn't issued a detailed formal response addressing the specific technical accusations. But we can infer what their perspective likely is based on statements from Chinese tech companies in similar situations.

Deep Seek would probably argue that:

Model distillation is a legitimate research technique widely used by academics and companies globally. They're applying a standard industry practice.
They're not violating any law. Terms of service violations are contractual issues, not crimes. Most tech contracts have been violated at some point. The question is whether the violation is serious enough to warrant legal action.
Extraction is inevitable in competitive markets. If Anthropic won't sell Claude to Chinese companies, and Chinese companies want to develop competitive AI, they have to build independently or extract. This is standard competitive dynamics.
Western companies do similar things. Open AI built GPT on top of techniques developed at Stanford and other research institutions. Google trained their models on data scraped from the web. The criticism of Deep Seek for doing something similar is hypocritical.
Geopolitical double standards exist. American companies get to build proprietary models and restrict access. When Chinese companies try to build competitive models, it's framed as theft. The real issue is that the West wants to maintain technological dominance, not that extraction is inherently wrong.

Some of these arguments have merit. Distillation is legitimate. Competitive extraction is normal in tech. Geopolitical double standards do exist.

But that doesn't change the fundamental issue: Deep Seek allegedly violated Anthropic's terms of service at scale and misrepresented itself through fraudulent accounts. Whether you think that's morally wrong depends partly on how you view geopolitical fairness and partly on how you view contract law.

Deep Seek's own capabilities are genuinely impressive. They built efficient models that are competitive with American frontier models while using less computational resources. Whether those capabilities came from independent development, extraction, or some combination isn't something external observers can definitively know. But the extraction campaign, if accurately described, is real as reported by Anthropic.

The Emerging AI Security Standard

This situation is establishing a new baseline for how frontier AI companies will interact with each other and with regulators going forward.

The playbook is becoming clear:

Build sophisticated fraud detection to identify extraction attempts
Document violations in detail with specific numbers and evidence
Issue public statements that frame the issue as national security, not just competition
Coordinate with other companies in the same ecosystem
Loop in regulators and lawmakers to create political pressure
Advocate for restrictions that would make extraction harder (chip access limits, export controls, etc.)

This is smart strategy. It turns a contractual dispute (terms of service violation) into a national security issue, which gets government attention and can result in regulations that advantage American companies over Chinese competitors as noted by Google Cloud.

For frontier AI companies, the baseline operating procedure will increasingly involve:

Account security: Multi-factor authentication, behavioral verification, sophisticated fraud detection
Rate limiting: Restricting query volume per account and aggregate limits per entity
Query analysis: Machine learning models that identify suspicious patterns in what's being asked
Output modification: Possibly watermarking or other techniques that subtly degrade distillability
Geographic restrictions: Limiting access from certain regions
Usage auditing: Logging what users are doing and with what frequency
Enforcement: Being willing to ban users, restrict access, and pursue legal action

The cost of all this is real. It makes AI APIs more expensive to operate. It introduces friction for legitimate users. It requires hiring security engineers and building infrastructure specifically for defense.

But in a world where competitors will systematically try to extract your capabilities, you have no choice. The alternative is watching your competitive advantages disappear within months as reported by Anthropic.

Future Implications: What Happens Next

Looking forward, several scenarios are likely:

Regulatory action: Expect the U. S. government to propose regulations or restrictions on AI model access, training data use, and international transfer of models. These will likely favor American companies that already have capital and compliance infrastructure.

Export controls: Chip access restrictions will probably expand. If you can't train large models without specific advanced semiconductors, and those semiconductors are restricted to American companies and allies, that limits who can build competitive AI models globally.

Legal battles: Anthropic will likely pursue legal action against the companies involved. These will set precedent for how terms of service violations in AI are treated under contract law and potentially copyright law.

Industry standards: We'll probably see industry-wide standards around fraud detection, account security, and API usage monitoring. This will be formalized through technical standards bodies and industry associations.

Open source momentum: Some companies will respond to these security challenges by open-sourcing their models. Meta has already moved toward open-source with Llama. If proprietary models can be extracted, the open-source approach avoids that problem.

Chinese capability acceleration: Despite (or because of) the restriction attempts, Chinese AI capabilities will continue improving. The combination of domestic investment, extraction from American models, and continued research will result in competitive Chinese AI systems. The question is how quickly as reported by Anthropic.

Geopolitical fragmentation: We're moving toward a world where there are separate AI ecosystems: American/Western AI systems, Chinese AI systems, potentially European AI systems. These ecosystems will compete, cooperate selectively, and develop different standards as discussed by Brookings.

The Deep Seek distillation incident is a milestone in this process. It's not the first extraction attempt, but it's the most publicly documented and the most consequential in terms of triggering industry-wide response.

What You Should Do If You Build AI Products

If you're building AI products or operating API services, this situation has practical implications for how you should operate.

Monitor for extraction attempts: Implement fraud detection that specifically looks for signs of systematic distillation. This means monitoring for:

Coordinated account creation
Bulk query patterns
Queries designed to extract specific capabilities
Response harvesting and storage
Unusual geographic or temporal patterns

Lock down your terms of service: Be explicit about what users can and can't do with your outputs. Make it clear that training competing models is prohibited. Define penalties for violation. Make terms easy to access and understand.

Plan for inevitable violations: You can't prevent all extraction, but you can detect it, document it, and respond. Have a playbook for how you'll respond to discovered violations: warnings, account suspension, legal action.

Invest in security: Budget for fraud detection, account security, behavioral analysis, and compliance. This is now a core infrastructure requirement for frontier AI services.

Consider watermarking: Research techniques for subtly marking your outputs in ways that don't affect user experience but make them less useful for distillation. This is an arms race, but it's worth participating in.

Stay ahead on capability: The best defense is speed. If you can improve your models faster than competitors can extract them, you maintain competitive advantage. Focus on continuous improvement and capability expansion.

Engage regulators proactively: If you think extraction is happening, document it thoroughly and share that information with regulators. Help shape policy in ways that protect your interests.

Build community: Coordinate with other companies in your ecosystem. When multiple companies face similar threats, collective action through industry associations is more effective than individual complaints as noted by Google Cloud.

The Ethical Questions Nobody's Asking

Amidst the national security rhetoric and competitive accusations, there are some deeper ethical questions worth considering.

First: Is it fair to restrict Chinese companies from accessing American AI technology? The U. S. argues it's necessary for national security. China argues it's technological protectionism masquerading as security. Both arguments have merit. If you genuinely believe unfettered AI capability in authoritarian hands is dangerous, restrictions make sense. But if you think technological access should be democratized globally, the restrictions seem unfair.

Second: Should companies be able to keep powerful AI models proprietary? If you believe frontier AI is important infrastructure that should be publicly available, proprietary models are problematic. But if you believe companies have the right to protect their intellectual property, proprietary models are justified. There's no obvious right answer here.

Third: Is extraction really wrong if the original model builder was restricting access? If Anthropic won't let Chinese researchers use Claude, can they complain when Chinese researchers find ways to access Claude anyway? From one perspective, restricted access is artificial scarcity that harms researchers and innovation. From another, it's protecting intellectual property.

Fourth: What's the right balance between security and transparency? Proprietary models can be secured and restricted. Open-source models can't. Is national security best served by restricted, controllable AI or by transparent, auditable AI? There's genuine disagreement on this.

These questions don't have clean answers. But they're worth asking. The conversation about AI security often skips over these deeper tensions in favor of blaming individual companies for bad behavior as discussed by Brookings.

Conclusion: A Shifting AI Landscape

The Anthropic vs. Deep Seek situation is more than corporate drama or international tensions. It's a signal that the era of uncontested American dominance in frontier AI is ending.

For years, American companies like Open AI and Anthropic could release powerful models and assume they'd maintain competitive advantage through speed, capital, and talent. The assumption was that by the time competitors caught up independently, American companies would have already moved further ahead.

Distillation breaks that model. It compresses the timeline for competitive capabilities from years to months. And if extraction works at scale, as Anthropic alleges, then proprietary advantages become much harder to maintain as reported by Anthropic.

This creates a few possible futures:

The restricted future: Governments impose limitations on AI development (chip access, export controls, etc.) that effectively lock competitive AI development into a few countries. America and allies maintain dominance through restriction rather than superiority.

The open-source future: Companies shift toward open-source models, accepting that proprietary advantage is impossible. Competition focuses on implementation, data, and fine-tuning rather than base model capability.

The fragmented future: Different regions develop separate AI ecosystems with different capabilities, standards, and values. The global AI market splinters into competitive blocs.

The acceleration future: Companies invest heavily in security but the arms race continues. Distillation techniques improve, detection gets better, and the game intensifies. Capability advancement accelerates for everyone because of the competitive pressure.

Most likely, we get some combination of all four. Governments will restrict some things (chips, exports). Some companies will open-source. The market will fragment regionally. And everyone will accelerate innovation to stay ahead.

For practitioners, researchers, and companies building AI systems, the message is clear: security is no longer optional, competitors will attempt extraction at scale, and the regulatory environment will get significantly more complex.

The Anthropic vs. Deep Seek situation is the beginning of a new phase in AI competition. We're moving from a period of rapid innovation with light security to a period of innovation with much tighter control, regulation, and geopolitical boundaries.

How that plays out over the next few years will shape the entire AI industry's trajectory as noted by Anthropic.