Artificial Intelligence Law: Regulating the Great Unbundling
The New Code of Existence: Navigating Artificial Intelligence Law
Can legal frameworks designed for human actors effectively govern technologies that operate at a scope, speed, and intelligence that dwarfs our own? This is no longer a hypothetical question. As artificial intelligence becomes deeply embedded in our economic and social lives, we are in a global race to write the rules for a new kind of existence—one where the fundamental components of human capability are being systematically unbundled.
This process, what I explore in my book "The Great Unbundling," is the central challenge of our time. For millennia, our societies and laws were built on the assumption of a "bundled" human. A person who possesses analytical intelligence also holds emotional intelligence, directs their own physical actions, and—critically—experiences the consequences. AI shatters this model. It isolates intelligence from consciousness, action from intent, and outcome from accountability.
Artificial intelligence law, therefore, is more than a new legal specialty; it is humanity's collective attempt to impose order on this rapid deconstruction. It's the framework through which we will decide which parts of the human bundle are worth protecting and how to manage intelligences that operate outside of it.
This page serves as your comprehensive guide to this shifting landscape.
- For the AI-Curious Professional, it provides a clear map of the major regulations, like the EU AI Act, that will define compliance and market access.
- For the Philosophical Inquirer, it connects these new laws to deeper questions about value, liability, and what it means to be human when our unique capabilities are no longer unique.
- For the Aspiring AI Ethicist, it offers a substantiated look at the critical legal issues—from bias to intellectual property—that will define the field for decades to come.
The Unbundling of Governance: Why Traditional Law Falls Short
Our legal systems are predicated on a simple, powerful idea: the accountable agent. We assign liability and intent to a person or a corporate entity, a bundled actor who makes a decision and is responsible for the result. Artificial intelligence fundamentally breaks this chain of accountability.
Consider a loan-denial algorithm that exhibits discriminatory bias. Who is legally responsible?
- The developers who wrote the initial code?
- The company that trained the model on biased historical data?
- The AI system itself, which learned and evolved its decision-making criteria in ways no single human directed?
As J.Y. Sterling argues in The Great Unbundling, this is the core challenge: "We are trying to apply rules based on human intent to systems that have unbundled capability from intent." The profit-driven engine of capitalism finances this unbundling at a pace that legislative bodies, by their deliberative nature, cannot match. This creates a dangerous gap between technological capability and regulatory oversight, a gap that global powers are now scrambling to close.
The Global Race to Regulate: Key Artificial Intelligence Laws & Frameworks
The attempt to govern AI is not uniform. Around the world, different philosophical and political approaches are emerging, creating a complex patchwork of artificial intelligence legislation.
The European Union's Landmark AI Act: A Risk-Based Blueprint
The most comprehensive effort to date is the European Union AI Act. Rather than regulating the technology itself, the EU AI Act regulates its application based on the level of risk it poses to the health, safety, and fundamental rights of individuals. It establishes a pyramid of risk:
- Unacceptable Risk: These AI systems are banned entirely. Examples include government-run social scoring and AI that uses manipulative subliminal techniques.
- High-Risk: This is the most regulated category, covering AI used in critical infrastructure, medical devices, employment decisions, and law enforcement. These systems face strict requirements for risk management, data quality, human oversight, and transparency before they can enter the market.
- Limited Risk: Systems like chatbots must comply with transparency obligations, ensuring users know they are interacting with an AI.
- Minimal Risk: The vast majority of AI applications, such as AI-enabled video games or spam filters, fall into this category with no new legal obligations.
The EU AI Act entered into force in August 2024, with key provisions, especially those concerning General-Purpose AI (GPAI) models, becoming applicable on August 2, 2025. This act has significant "extraterritorial reach," meaning any company offering AI services within the EU must comply, making it a de facto global standard.
The United States' Patchwork Approach: Federal Ambition vs. State AI Laws
The U.S. has adopted a more sector-specific and fragmented approach. The landmark White House Executive Order on Safe, Secure, and Trustworthy AI sets a national strategy but relies heavily on existing authorities and the development of new standards.
A cornerstone of the U.S. strategy is the NIST AI Risk Management Framework (AI RMF). This voluntary framework provides a structured process for organizations to Govern, Map, Measure, and Manage AI risks. It is designed to be adaptable across industries and promotes the development of trustworthy and responsible AI.
While comprehensive federal AI legislation has been slow to materialize, a flurry of activity is happening at the state level. States like Colorado, Texas, and California are leading the way with laws targeting AI-driven discrimination in hiring and insurance, mandating transparency in automated decision-making, and protecting consumer privacy. This creates a complex compliance map for businesses operating nationwide.
China's State-Centric Model: A Dual Focus on Control and Development
China's approach to AI regulation is intertwined with its national strategic goals. It has implemented some of the world's earliest and most specific rules, particularly for generative AI and recommendation algorithms. These regulations prioritize state control, content censorship, and aligning AI development with socialist values. This model presents a stark contrast to the rights-based approach of the EU and the market-driven approach of the U.S., highlighting the deep philosophical divides in the global conversation about who controls AI.
Unbundling Justice: Key Legal Issues in the Age of AI
Beyond broad frameworks, artificial intelligence law is grappling with specific, complex legal challenges that cut to the heart of the "Great Unbundling."
Intellectual Property: Who Owns What an AI Creates?
When a generative AI creates a compelling image, a piece of music, or a block of code, who owns the copyright? Current legal doctrine ties authorship to a human creator. AI unbundles creativity from human consciousness, creating a legal vacuum. High-profile lawsuits against AI developers for training their models on copyrighted data without permission are testing the limits of "fair use," forcing us to question whether an algorithm can truly be an author.
Bias and Discrimination: Can We Code Fairness?
AI systems learn from data, and if that data reflects historical human biases, the AI will not only replicate but often amplify them. Statistics have shown that AI recruiting tools can exhibit significant bias. A survey by The Harris Poll for the American Staffing Association found that 49% of employed U.S. job seekers believe AI recruiting tools are more biased than humans. This is the unbundling of problem-solving from ethical understanding. An AI can optimize for "qualified candidates" based on past data without understanding the concept of fairness, leading to discriminatory outcomes that are illegal under civil rights laws. Addressing this requires a deep rethinking of data governance and algorithmic accountability.
Privacy and Surveillance: The End of Anonymity?
AI-powered technologies, particularly facial recognition and behavioral analysis, unbundle identity from physical presence. They allow for the tracking and analysis of individuals at an unprecedented scale. This raises profound questions for privacy. As discussed in Why Does AI Not Keep Information Secure, the massive datasets required to train AI are themselves valuable targets. AI privacy laws, often built upon existing frameworks like GDPR, are attempting to give individuals more control, but the technological capabilities often outpace the legal protections.
The Great Re-bundling: Law as a Tool for Human-Centric AI
The rise of artificial intelligence law should not be seen merely as a restrictive force. It is our most powerful tool for shaping the future, a conscious act of what The Great Unbundling calls "The Great Re-bundling."
By mandating transparency, we are forcing a re-bundling of AI's intelligence with human understanding. By requiring "human-in-the-loop" protocols for high-stakes decisions, we re-bundle automated calculation with human judgment and accountability. This is where effective AI Risk Management and robust AI Compliance programs become essential, not just as legal requirements, but as ethical imperatives.
What This Means for You: Navigating the New Legal Landscape
The era of treating AI as an unregulated frontier is over.
- For Professionals and Business Leaders: Your starting point is understanding your obligations. Begin with the NIST AI Risk Management Framework to assess your internal processes. If you operate in Europe, achieving compliance with the EU AI Act is a critical, non-negotiable priority.
- For Thinkers and Ethicists: The debate is just beginning. Engage with the profound questions this new legal field raises. Does an AI deserve rights? (See our exploration of Artificial Intelligence Rights). How do we define legal personhood when intelligence is no longer exclusively human?
- For Every Citizen: Demand transparency and accountability. Ask how automated systems are making decisions that affect your life—from your credit score to your job application. Support policies that prioritize human values in an increasingly automated world.
The laws we write today will determine the relationship between humans and artificial intelligence for the next century. They are the front line in the struggle to define the value of a human being in a world where our intelligence is no longer our own.
To explore the economic, philosophical, and social forces driving the need for AI regulation, dive deeper into J.Y. Sterling's foundational book, The Great Unbundling. Sign up for our newsletter for continuous, insightful analysis on the future of AI and humanity.