Anthropomorphizing AI: Dire consequences of mistaking human-like for human have already emerged

In our rush to understand and relate to AI, we have fallen into a seductive trap: Attributing human characteristics to these robust but fundamentally non-human systems. This anthropomorphizing of AI is not just a harmless quirk of human nature — it is becoming an increasingly dangerous tendency that might cloud our judgment in critical ways. Business leaders are comparing AI learning to human education to justify training practices to lawmakers crafting policies based on flawed human-AI analogies. This tendency to humanize AI might inappropriately shape crucial decisions across industries and regulatory frameworks.

Viewing AI through a human lens in business has led companies to overestimate AI capabilities or underestimate the need for human oversight, sometimes with costly consequences. The stakes are particularly high in copyright law, where anthropomorphic thinking has led to problematic comparisons between human learning and AI training.

The language trap

Listen to how we talk about AI: We say it “learns,” “thinks,” “understands” and even “creates.” These human terms feel natural, but they are misleading. When we say an AI model “learns,” it is not gaining understanding like a human student. Instead, it performs complex statistical analyses on vast amounts of data, adjusting weights and parameters in its neural networks based on mathematical principles. There is no comprehension, eureka moment, spark of creativity or actual understanding — just increasingly sophisticated pattern matching.

This linguistic sleight of hand is more than merely semantic. As noted in the paper, Generative AI’s Illusory Case for Fair Use: “The use of anthropomorphic language to describe the development and functioning of AI models is distorting because it suggests that once trained, the model operates independently of the content of the works on which it has trained.” This confusion has real consequences, mainly when it influences legal and policy decisions.

The cognitive disconnect

Perhaps the most dangerous aspect of anthropomorphizing AI is how it masks the fundamental differences between human and machine intelligence. While some AI systems excel at specific types of reasoning and analytical tasks, the large language models (LLMs) that dominate today’s AI discourse — and that we focus on here — operate through sophisticated pattern recognition.

These systems process vast amounts of data, identifying and learning statistical relationships between words, phrases, images and other inputs to predict what should come next in a sequence. When we say they “learn,” we’re describing a process of mathematical optimization that helps them make increasingly accurate predictions based on their training data.

Consider this striking example from research by Berglund and his colleagues: A model trained on materials stating “A is equal to B” often cannot reason, as a human would, to conclude that “B is equal to A.” If an AI learns that Valentina Tereshkova was the first woman in space, it might correctly answer “Who was Valentina Tereshkova?” but struggle with “Who was the first woman in space?” This limitation reveals the fundamental difference between pattern recognition and true reasoning — between predicting likely sequences of words and understanding their meaning.

The copyright conundrum

This anthropomorphic bias has particularly troubling implications in the ongoing debate about AI and copyright. Microsoft CEO Satya Nadella recently compared AI training to human learning, suggesting that AI should be able to do the same if humans can learn from books without copyright implications. This comparison perfectly illustrates the danger of anthropomorphic thinking in discussions about ethical and responsible AI.

Some argue that this analogy needs to be revised to understand human learning and AI training. When humans read books, we do not make copies of them — we understand and internalize concepts. AI systems, on the other hand, must make actual copies of works — often obtained without permission or payment — encode them into their architecture and maintain these encoded versions to function. The works don’t disappear after “learning,” as AI companies often claim; they remain embedded in the system’s neural networks.

The business blind spot

Anthropomorphizing AI creates dangerous blind spots in business decision-making beyond simple operational inefficiencies. When executives and decision-makers think of AI as “creative” or “intelligent” in human terms, it can lead to a cascade of risky assumptions and potential legal liabilities.

Overestimating AI capabilities

One critical area where anthropomorphizing creates risk is content generation and copyright compliance. When businesses view AI as capable of “learning” like humans, they might incorrectly assume that AI-generated content is automatically free from copyright concerns. This misunderstanding can lead companies to:

• Deploy AI systems that inadvertently reproduce copyrighted material, exposing the business to infringement claims
• Fail to implement proper content filtering and oversight mechanisms
• Assume incorrectly that AI can reliably distinguish between public domain and copyrighted material
• Underestimate the need for human review in content generation processes

The cross-border compliance blind spot

The anthropomorphic bias in AI creates dangers when we consider cross-border compliance. As explained by Daniel Gervais, Haralambos Marmanis, Noam Shemtov, and Catherine Zaller Rowland in “The Heart of the Matter: Copyright, AI Training, and LLMs,” copyright law operates on strict territorial principles, with each jurisdiction maintaining its own rules about what constitutes infringement and what exceptions apply.

This territorial nature of copyright law creates a complex web of potential liability. Companies might mistakenly assume their AI systems can freely “learn” from copyrighted materials across jurisdictions, failing to recognize that training activities that are legal in one country may constitute infringement in another. The EU has recognized this risk in its AI Act, particularly through Recital 106, which requires any general-purpose AI model offered in the EU to comply with EU copyright law regarding training data, regardless of where that training occurred.

This matters because anthropomorphizing AI’s capabilities can lead companies to underestimate or misunderstand their legal obligations across borders. The comfortable fiction of AI “learning” like humans obscures the reality that AI training involves complex copying and storage operations that trigger different legal obligations in other jurisdictions. This fundamental misunderstanding of AI’s actual functioning, combined with the territorial nature of copyright law, creates significant risks for businesses operating globally.

The human cost

One of the most concerning costs is the emotional toll of anthropomorphizing AI. We see increasing instances of people forming emotional attachments to AI chatbots, treating them as friends or confidants. This can be particularly dangerous for vulnerable individuals who might share personal information or rely on AI for emotional support it cannot provide. The AI’s responses, while seemingly empathetic, are sophisticated pattern matching based on training data — there is no genuine understanding or emotional connection.

This emotional vulnerability could also manifest in professional settings. As AI tools become more integrated into daily work, employees might develop inappropriate levels of trust in these systems, treating them as actual colleagues rather than tools. They might share confidential work information too freely or hesitate to report errors out of a misplaced sense of loyalty. While these scenarios remain isolated right now, they highlight how anthropomorphizing AI in the workplace could cloud judgment and create unhealthy dependencies on systems that, despite their sophisticated responses, are incapable of genuine understanding or care.

Breaking free from the anthropomorphic trap

So how do we move forward? First, we need to be more precise in our language about AI. Instead of saying an AI “learns” or “understands,” we might say it “processes data” or “generates outputs based on patterns in its training data.” This is not just pedantic — it helps clarify what these systems do.

Second, we must evaluate AI systems based on what they are rather than what we imagine them to be. This means acknowledging both their impressive capabilities and their fundamental limitations. AI can process vast amounts of data and identify patterns humans might miss, but it cannot understand, reason or create in the way humans do.

Finally, we must develop frameworks and policies that address AI’s actual characteristics rather than imagined human-like qualities. This is particularly crucial in copyright law, where anthropomorphic thinking can lead to flawed analogies and inappropriate legal conclusions.

The path forward

As AI systems become more sophisticated at mimicking human outputs, the temptation to anthropomorphize them will grow stronger. This anthropomorphic bias affects everything from how we evaluate AI’s capabilities to how we assess its risks. As we have seen, it extends into significant practical challenges around copyright law and business compliance. When we attribute human learning capabilities to AI systems, we must understand their fundamental nature and the technical reality of how they process and store information.

Understanding AI for what it truly is — sophisticated information processing systems, not human-like learners — is crucial for all aspects of AI governance and deployment. By moving past anthropomorphic thinking, we can better address the challenges of AI systems, from ethical considerations and safety risks to cross-border copyright compliance and training data governance. This more precise understanding will help businesses make more informed decisions while supporting better policy development and public discourse around AI.

The sooner we embrace AI’s true nature, the better equipped we will be to navigate its profound societal implications and practical challenges in our global economy.

Roanie Levy is licensing and legal advisor at CCC.