The global artificial intelligence landscape has entered a peculiar phase where the most advanced AI systems, developed at enormous cost by American companies, are being used as teachers to train their potential competitors. This phenomenon has sparked intense debate in both technical and policy circles, particularly as it relates to Chinese AI development efforts operating under increasingly strict export controls on advanced computing hardware.
In late 2024 and early 2025, concerns intensified when researchers and industry observers documented how Chinese AI laboratories were systematically querying models like OpenAI's GPT-4, Anthropic's Claude, and Google's Gemini to generate training data for their own systems. DeepSeek, a Chinese AI company, became a focal point of this discussion when it released models that demonstrated capabilities approaching frontier systems while claiming to have trained them at a fraction of the typical cost. The company's DeepSeek-V3 model, released in late 2024, sparked particular interest because it appeared to achieve strong performance despite China's limited access to cutting-edge Nvidia chips like the H100, which are restricted under U.S. export controls.
The situation represents a fascinating paradox in the AI industry. American companies have built their models through massive investments in computing infrastructure, data collection, and research talent. These models are then made available through APIs, application programming interfaces that allow anyone with an internet connection and payment method to send queries and receive responses. While these APIs generate revenue and enable widespread beneficial use, they simultaneously create a pathway for competitors to extract knowledge from the models without bearing the full cost of original development.
UNDERSTANDING MODEL DISTILLATION: TEACHING STUDENTS FROM EXPERT TEACHERS
Model distillation, in its essence, is a technique where a smaller, more efficient model learns to mimic the behavior of a larger, more capable model. The concept draws inspiration from how human students learn from expert teachers. Just as a student doesn't need to independently discover all of calculus but can learn it more efficiently from a knowledgeable instructor, a smaller AI model can learn to approximate the responses of a larger model without retracing all the computational steps that went into training the larger system.
The technical process works through a carefully orchestrated data generation and training pipeline. Consider a concrete example of how this might work in practice. Suppose a Chinese research team wants to build a model capable of answering questions about physics, writing code, and engaging in logical reasoning. Rather than collecting billions of examples from the internet and spending months training a massive model from scratch, they could take a different approach.
First, they would generate a diverse set of prompts or questions covering the domains they care about. These might include questions like "Explain quantum entanglement in simple terms," "Write a Python function to sort a list using merge sort," or "Analyze the logical validity of this argument." They would then send these prompts to a frontier model like GPT-4 or Claude through the standard API, paying the normal usage fees. The frontier model would generate detailed, high-quality responses to each prompt.
This process creates what researchers call a "synthetic dataset," a collection of prompt-response pairs where the responses come from the advanced model rather than from human experts or naturally occurring text. The team might generate hundreds of thousands or even millions of such pairs, systematically covering different topics, difficulty levels, and response styles.
Here is a simplified illustration of a single distillation interaction:
INPUT PROMPT (sent to frontier model): "Explain how transformers work in neural networks"
FRONTIER MODEL RESPONSE (GPT-4 or Claude): "Transformers are a neural network architecture that processes sequential data through attention mechanisms. Unlike recurrent networks that process tokens one at a time, transformers can attend to all positions simultaneously..." [detailed 500-word explanation continues]
STUDENT MODEL TRAINING: The smaller model is then trained to produce similar outputs when given the same inputs, learning to approximate the frontier model's knowledge and reasoning patterns.
The student model, which might be significantly smaller than the teacher, is then trained on this synthetic dataset. During training, the model learns to predict the teacher's responses when given the same prompts. Through exposure to hundreds of thousands of these examples, the student model begins to internalize patterns of reasoning, factual knowledge, and response styles that characterize the teacher model.
What makes this approach particularly powerful is that the student model doesn't just memorize the specific examples. Instead, it learns general patterns that allow it to respond appropriately to new prompts it has never seen before. If the synthetic dataset is sufficiently diverse and the student model has adequate capacity, it can develop capabilities that generalize beyond its training examples, effectively capturing a compressed version of the teacher's knowledge.
The distillation process can be refined through several techniques. One approach involves using the teacher model's confidence scores or probability distributions over possible responses, not just its final answers. This provides richer training signal, allowing the student to learn not just what the teacher says but how confident it is about different aspects of its response. Another technique involves iterative refinement, where the student model's outputs are compared to the teacher's, and the student is repeatedly adjusted to minimize the differences.
THE MECHANICS OF ACCESS: HOW CHINESE COMPANIES REACH FRONTIER MODELS
Despite geopolitical tensions and increasing awareness of the distillation issue, Chinese companies and researchers maintain relatively straightforward access to American frontier models. The primary access mechanism is the same one available to users worldwide: public APIs offered by OpenAI, Anthropic, Google, and other providers. These APIs were designed to democratize access to advanced AI capabilities, allowing developers, researchers, and businesses to integrate powerful language models into their applications without building such systems from scratch.
The business model of these API providers creates an interesting tension. On one hand, they generate substantial revenue from API usage, with customers paying based on the number of tokens (roughly corresponding to words) they process. A research team generating a large synthetic dataset might process millions or tens of millions of tokens, translating to thousands or tens of thousands of dollars in API fees. This represents meaningful revenue for the API providers.
On the other hand, these same API calls enable competitors to extract knowledge from models that cost hundreds of millions of dollars to develop. OpenAI reportedly spent over $100 million training GPT-4, while some estimates for training runs of the largest models exceed several hundred million dollars when accounting for computing infrastructure, electricity, and the opportunity cost of using scarce high-end GPUs. The API fees paid by distillation efforts represent a tiny fraction of these development costs.
Several factors make it difficult for API providers to prevent distillation-focused usage. First, distinguishing between legitimate use and distillation is technically challenging. A researcher using the API to generate training data for distillation sends queries that look identical to a developer testing their application or a student seeking homework help. The queries themselves don't carry obvious markers of their intended purpose.
Second, even when providers implement usage limits or monitoring systems, determined users can circumvent them through various means. They might create multiple accounts, route requests through different IP addresses, or space out their queries to avoid triggering rate limits. Some reports suggest that Chinese research teams have used intermediaries or international collaborators to access APIs, further obscuring the ultimate destination and purpose of the queries.
Third, the global nature of internet services means that even if a company wanted to block access from specific countries, doing so would be technically complex and potentially counterproductive. Many legitimate users, including international researchers, students, and businesses, would be affected by geographic restrictions. Moreover, users could easily circumvent such blocks using virtual private networks or proxy servers.
The situation is further complicated by the fact that some frontier models are released as open weights, meaning the model parameters are publicly available for anyone to download and use. Meta's Llama series, for instance, has been released with relatively permissive licenses. While Meta restricts commercial use by entities with large user bases, the models themselves can be freely downloaded and used for research, including as teachers for distillation. This creates an even more direct pathway for knowledge transfer, as users don't even need to pay API fees or worry about rate limits.
THE GPU PARADOX: WHY DISTILLATION STILL REQUIRES SUBSTANTIAL COMPUTING POWER
A common misconception about model distillation is that it eliminates the need for advanced computing hardware. In reality, while distillation can reduce computational requirements compared to training a frontier model from scratch, it still demands significant GPU resources, particularly when the goal is to create a capable student model.
The computing requirements arise from several aspects of the distillation process. First, generating the synthetic dataset itself can be computationally intensive if done at scale. While querying an API doesn't require GPUs on the user's end, some distillation approaches involve running the teacher model locally, especially if the team has access to open-weight models. Running inference on large models, even just to generate outputs, requires substantial memory and computational capacity.
Second, and more significantly, training the student model requires extensive GPU computation. Even though the student model might be smaller than the frontier teacher, it still needs to be large enough to capture meaningful portions of the teacher's capabilities. A student model with tens of billions of parameters, while smaller than frontier models with hundreds of billions or trillions of parameters, still requires clusters of high-end GPUs to train effectively.
Consider the computational demands in concrete terms. Training a model with 30 billion parameters on a dataset of millions of examples might require several thousand GPU-hours on advanced chips. If a research team has access to a cluster of 100 high-end GPUs, this might translate to days or weeks of continuous training. The GPUs must have sufficient memory to hold the model parameters, gradients, and optimizer states during training, which typically requires chips with at least 40 or 80 gigabytes of memory.
This is where U.S. export controls create complications for Chinese AI development. The most capable GPUs for AI training, such as Nvidia's H100 and A100 chips, are subject to export restrictions to China. These controls aim to limit China's ability to develop advanced AI systems, particularly those with potential military applications. However, the restrictions have proven difficult to enforce completely.
Chinese companies have pursued several strategies to obtain necessary computing power despite these restrictions. Some have stockpiled chips before restrictions took effect, accumulating reserves of advanced GPUs. Others have turned to less restricted but still capable alternatives, such as Nvidia's A800 and H800 chips, which were specifically designed to meet export control thresholds while still offering substantial performance. When these chips were also restricted, companies explored domestic alternatives, though Chinese-manufactured AI chips generally lag behind Nvidia's offerings in performance and efficiency.
Another approach involves maximizing the efficiency of available hardware through algorithmic innovations. DeepSeek, for instance, has claimed to achieve strong results through techniques like mixture-of-experts architectures, which activate only portions of the model for each input, reducing computational requirements. The company has also emphasized training efficiency, suggesting they can achieve competitive performance with fewer training steps and less compute than typical frontier model development.
The GPU requirements for distillation create an interesting dynamic. While distillation allows Chinese companies to benefit from American AI research without bearing the full cost of original development, they still need substantial computing infrastructure to make effective use of the knowledge they extract. This means that export controls on advanced chips do impose meaningful constraints, even if they don't completely prevent AI development. The constraints force Chinese developers to be more creative and efficient, potentially leading to innovations in training techniques and model architectures.
ETHICAL CONCERNS AND THE THEFT ANALOGY: WHY DISTILLATION RAISES TROUBLING QUESTIONS
The practice of using frontier models to train competing systems through distillation has sparked heated debate about intellectual property, fair competition, and the ethics of AI development. Critics often characterize the practice using language associated with theft, arguing that it represents an illegitimate appropriation of value created through massive investment and innovation.
The theft analogy rests on several observations about the economics and ethics of distillation. First, frontier model developers invest enormous resources in creating their systems. These investments include not just the direct costs of computing and electricity but also years of research into architectures, training techniques, and safety measures. OpenAI, Anthropic, and Google have collectively spent billions of dollars developing their models and the infrastructure to train them.
When a competitor uses distillation to create a similar model at a fraction of the cost, they benefit from all this investment without contributing to it. They effectively free-ride on the research and development efforts of the original creators. The student model inherits knowledge and capabilities that took years and billions of dollars to develop, but the distillation team might spend only months and millions of dollars to capture much of this value.
Consider an analogy to traditional industries. Imagine a pharmaceutical company spending a decade and billions of dollars developing a new drug, conducting clinical trials, and navigating regulatory approval. Now imagine a competitor analyzing the drug's effects, reverse-engineering its therapeutic properties, and creating a similar medication without conducting their own trials or basic research. Most observers would consider this problematic, even if the competitor didn't literally steal the chemical formula. The competitor would be appropriating the value of the original company's research investment.
The AI case has some important differences from this pharmaceutical analogy, but the core concern remains similar. The frontier model represents accumulated knowledge and capabilities developed through extensive effort. Distillation allows others to extract this knowledge through a process that feels less like independent innovation and more like copying.
Second, distillation can undermine the business models that fund frontier AI development. Companies like OpenAI and Anthropic have raised billions in investment based on the premise that their technological lead will translate to market dominance and eventual profitability. If competitors can quickly catch up through distillation, this premise becomes questionable. Why would investors fund expensive frontier research if the resulting advantages prove temporary and easily replicated?
This concern extends beyond private companies to questions of national competitiveness and security. U.S. policymakers have identified AI leadership as a strategic priority, both for economic competitiveness and national security. If American companies develop frontier models at great expense, only to see Chinese competitors rapidly close the gap through distillation, this undermines U.S. strategic objectives. The situation becomes particularly concerning if the resulting Chinese models are used for purposes contrary to American interests, such as surveillance systems or military applications.
Third, distillation raises questions about consent and the intended use of technology. When frontier model developers release APIs, they generally intend for these interfaces to enable beneficial applications: helping students learn, assisting programmers, powering creative tools, and so forth. Using the APIs to train competing models represents a use case that, while not explicitly prohibited in most terms of service, seems contrary to the spirit of making the technology available.
Some observers push back against the theft framing, arguing that it mischaracterizes what's actually happening. They note that distillation doesn't involve accessing proprietary code, stealing model weights, or breaching security systems. The distillers are using publicly available APIs in the manner they were designed to be used: sending inputs and receiving outputs. If the API providers didn't want this use case, the argument goes, they should have designed their systems differently or written clearer terms of service.
Moreover, defenders of distillation point out that learning from existing systems is a fundamental part of how technology progresses. Every AI researcher studies previous models, learns from their architectures and training techniques, and builds on prior work. Distillation, in this view, is simply a more systematic version of this normal scientific process. The fact that it's efficient and cost-effective doesn't make it illegitimate.
There's also a question of whether the knowledge contained in AI models can or should be owned in the same way as traditional intellectual property. The models learn from vast amounts of public data, including websites, books, and other sources that the model developers didn't create. If the models themselves are derivative works built on public knowledge, the argument goes, then using them to train other models is simply another step in the chain of knowledge building.
These competing perspectives highlight the genuinely difficult questions at the heart of the distillation debate. The practice exists in a gray area between clearly legitimate learning from prior work and clearly illegitimate theft of proprietary technology. The lack of clear legal and ethical frameworks for this scenario reflects the novelty of AI as a technology and the speed at which the field is evolving.
COUNTERMEASURES AND THEIR LIMITATIONS: THE CHALLENGE OF PREVENTING DISTILLATION
As awareness of distillation-based knowledge transfer has grown, frontier model developers and policymakers have explored various countermeasures to prevent or limit the practice. These efforts reveal both the technical ingenuity of defenders and the fundamental difficulties of preventing determined adversaries from extracting knowledge from publicly accessible systems.
One category of countermeasures involves technical modifications to how models respond to queries. Some researchers have proposed adding subtle watermarks or fingerprints to model outputs that would be detectable in student models trained on synthetic data from the teacher. The idea is that if a student model's responses contain these fingerprints, it would provide evidence of distillation and potentially enable legal action or public exposure.
However, watermarking approaches face significant challenges. For the watermark to be useful, it must be robust enough to survive the training process, meaning it needs to be present in the student model's outputs even after the model has learned general patterns from the synthetic data. At the same time, the watermark must be subtle enough that it doesn't degrade the quality of the teacher model's responses for legitimate users. Balancing these requirements is technically difficult.
Moreover, sophisticated distillers might be able to detect and remove watermarks if they become aware of them. They could train their student models to avoid reproducing the specific patterns that constitute the watermark, or they could post-process their model's outputs to remove suspicious patterns. This creates an arms race dynamic where defenders develop more sophisticated watermarks and attackers develop better detection and removal techniques.
Another technical approach involves deliberately degrading model outputs in ways that would harm distillation while minimally impacting legitimate use. For instance, a model might occasionally give slightly incorrect or incomplete answers, making the synthetic dataset noisier and less useful for training. However, this approach directly conflicts with the goal of providing high-quality service to paying customers. Users expect accurate, helpful responses, and deliberately reducing quality to prevent distillation would undermine the core value proposition of the API.
Some have proposed more sophisticated versions of this idea, where the model detects queries that seem designed for distillation and selectively degrades only those responses. For example, if a user sends thousands of diverse queries in a short time period, this might trigger defensive measures. The challenge is that many legitimate use cases, such as researchers conducting studies or companies processing large batches of data, also involve high-volume, diverse queries. Distinguishing between legitimate batch processing and distillation is not straightforward.
Rate limiting represents another defensive measure. By restricting how many queries a single user or account can make within a given time period, API providers can slow down the data generation process for distillation. If a team can only make a few thousand queries per day rather than millions, generating a large synthetic dataset becomes much more time-consuming.
However, rate limiting has obvious limitations. Determined users can create multiple accounts, use different payment methods, or route queries through various intermediaries to circumvent limits. The decentralized nature of the internet makes it difficult to enforce strict per-user limits when users can easily adopt new identities. Additionally, aggressive rate limiting would frustrate legitimate high-volume users, creating a trade-off between security and usability.
Legal and contractual measures represent another category of countermeasures. API providers could update their terms of service to explicitly prohibit using outputs for training competing models. This would provide a legal basis for action against violators, potentially including lawsuits or account termination.
The effectiveness of such legal measures depends heavily on enforcement. If a Chinese company violates terms of service, an American API provider might terminate their account, but the company could simply create new accounts or use intermediaries. Legal action across international borders is complex and often impractical, especially when the violating party is in a jurisdiction with different legal standards and limited cooperation with U.S. authorities.
Some observers have called for government intervention through export controls or other regulatory measures. Just as the U.S. government restricts exports of advanced chips, it could potentially restrict access to frontier AI models for entities in certain countries or those engaged in activities contrary to U.S. interests. However, implementing such controls for software services is far more challenging than for physical hardware.
Unlike chips, which must physically cross borders and can be inspected, API access occurs through digital communications that can be routed through multiple countries and anonymized through various technical means. Enforcing geographic restrictions on internet services has proven extremely difficult, as evidenced by the limited effectiveness of content blocking and censorship efforts. Users routinely circumvent such restrictions using VPNs, proxy servers, and other tools.
Moreover, broad restrictions on API access would have significant collateral damage. Many legitimate users in China and elsewhere would lose access to valuable tools. International research collaboration would be hampered. The measure might also accelerate the development of alternative models outside U.S. control, ultimately reducing American influence over the global AI ecosystem.
A more fundamental approach would involve rethinking the business model of frontier AI development. If making models available through APIs inevitably enables distillation, perhaps companies should rely less on API revenue and more on other monetization strategies. They might focus on proprietary applications built on their models, keep their most capable systems private, or develop specialized versions for specific industries that are harder to replicate through distillation.
However, this approach would require abandoning the vision of AI as a general-purpose platform that benefits from widespread access and diverse use cases. It would also potentially slow the beneficial applications of AI by limiting who can build on frontier capabilities. The tension between open access and competitive protection represents a genuine dilemma without easy resolution.
Some researchers have explored technical approaches that might allow models to be useful without revealing their full capabilities. For instance, models could be designed to provide helpful outputs while operating in a way that makes their internal reasoning opaque. However, this conflicts with growing demands for AI transparency and interpretability, which are seen as important for safety and accountability.
The most promising countermeasures likely involve combinations of technical, legal, and strategic approaches rather than any single solution. Watermarking combined with terms of service enforcement, rate limiting combined with monitoring for suspicious patterns, and strategic decisions about which capabilities to expose through APIs might collectively raise the cost and difficulty of distillation without completely preventing it.
Ultimately, the challenge of preventing distillation reflects a deeper tension in the AI industry between openness and control, between democratizing access to powerful technology and maintaining competitive advantages, between enabling beneficial uses and preventing harmful ones. This tension will likely persist as AI capabilities continue to advance and the strategic importance of AI leadership grows.
THE ROAD AHEAD: IMPLICATIONS FOR AI DEVELOPMENT AND GLOBAL COMPETITION
The phenomenon of model distillation and the specific case of Chinese companies using American frontier models as teachers illuminate broader questions about the future of AI development and global technological competition. These questions will shape the AI landscape for years to come and have implications extending far beyond the technical details of training methods.
One key question is whether the current model of frontier AI development is sustainable. The economics of spending hundreds of millions of dollars to train models that can then be partially replicated at far lower cost seem questionable from a business perspective. If distillation proves highly effective, it might undermine the incentive structure that currently drives massive investments in frontier research.
This could lead to several possible futures. In one scenario, frontier developers might retreat from open API access, keeping their most capable models proprietary and using them only for internal applications or carefully controlled partnerships. This would reduce the distillation risk but also limit the beneficial uses of AI and potentially slow innovation by reducing the number of developers who can build on cutting-edge capabilities.
In another scenario, the AI industry might evolve toward a more open model where knowledge sharing is accepted and even encouraged, with companies competing more on execution, specialized applications, and incremental improvements rather than on maintaining large capability gaps. This would represent a shift from the current dynamic where being at the frontier provides substantial advantages.
A third possibility is that technical countermeasures and legal frameworks evolve to make distillation more difficult or costly, allowing a middle ground where APIs remain available but knowledge extraction is limited. This would require innovations in both technology and policy that don't yet exist in mature form.
The geopolitical dimension adds another layer of complexity. The U.S.-China technology competition has made AI development a matter of national strategy, not just commercial competition. Both countries view AI leadership as crucial for economic competitiveness, military capability, and global influence. In this context, distillation becomes not just a business concern but a strategic issue.
Chinese success in developing capable models despite hardware restrictions and through techniques like distillation demonstrates both the difficulty of maintaining technological advantages through export controls alone and the innovative capacity of Chinese AI researchers. The situation suggests that long-term U.S. AI leadership will depend more on sustained innovation and investment than on preventing knowledge transfer.
For the global AI research community, the distillation debate raises questions about norms and practices. Should there be ethical guidelines around using frontier models to train competing systems? Should researchers disclose when their models were trained using synthetic data from other models? How should the community balance the benefits of open science and knowledge sharing against concerns about fair competition and strategic advantage?
These questions don't have obvious answers, and different stakeholders will reasonably disagree based on their values and interests. What seems clear is that the current situation, where distillation occurs widely but exists in a legal and ethical gray area, is unstable. Either norms and rules will develop to govern the practice, or the practice will reshape how frontier AI development works.
The distillation phenomenon also highlights the unique nature of AI as a technology. Unlike most previous technologies, AI systems can serve as teachers for other AI systems, creating unusual dynamics of knowledge transfer and competitive interaction. The fact that a model can be both a product and a teacher, both a source of value and a source of training data, creates complexities that don't exist for traditional software or hardware.
As AI capabilities continue to advance, these dynamics will likely intensify. More capable models will be more valuable targets for distillation, raising the stakes for both developers and distillers. At the same time, more capable models might also be better at detecting and resisting distillation attempts, potentially shifting the balance between offense and defense.
The coming years will reveal whether the AI industry can develop sustainable approaches to these challenges or whether the tensions between openness and control, between knowledge sharing and competitive protection, will force difficult choices that reshape how AI development works. The outcome will have profound implications not just for the companies and countries involved but for the broader trajectory of AI technology and its impact on society.
What began as a technical question about training methods has evolved into a complex issue touching on economics, ethics, law, and geopolitics. The story of model distillation and Chinese access to frontier AI is ultimately a story about how we navigate the challenges of developing powerful technologies in a competitive, interconnected world where knowledge flows across borders and the rules governing technological competition are still being written.
