When lawyers reference Aldous Huxley's Brave New World, they often treat "soma" as a literary symbol: a metaphor for the numbing effect of mindless entertainment. But soma is not just a metaphor; it is a product specification. It is a set of functional requirements: rapid, predictable relief from distress; minimal downside; and a secondary effect that is central, not incidental, which is social pacification.
As Artificial Intelligence (AI) becomes capable of optimizing human experience with greater precision, soma stops being a philosophy seminar and becomes a governance problem. Crucially, that governance problem is not limited to the digital. A superhuman scientific capability could plausibly contribute to a literal, pharmacological soma through generative chemistry and inverse molecular design (a subject that raises its own governance challenges at the intersection of IP, bioethics, and regulatory law). But the first widely deployed soma analogues will almost certainly arrive through recommendation systems, immersive media, and bewitching AI companions (link opens song). That is where entertainment lawyers need to focus.
The AI Governance Professional (AIGP) framework (link opens resources guide) is useful here not because it contains a special "Huxley chapter," but because it trains the governance posture that entertainment and media organizations increasingly need: establish expectations, apply policies across the lifecycle, govern development and deployment, and anchor decisions in legal and standards frameworks.
1. What Makes Digital Soma Different from "Bad Content"
Entertainment law has always grappled with content that is harmful, addictive, or manipulative. What makes the current moment different is the shift from content to system.
A piece of content, such as a film, a song, or a game, is static. It can be evaluated, rated, and regulated. But a recommendation engine, a generative content system, or an AI companion is not static. It is adaptive. It learns what keeps each individual user engaged, and it optimizes for that outcome at scale.
This is the difference between selling someone a drink and installing a tap in their living room that learns exactly when they are most likely to want one.
The governance challenge is not "is this content harmful?" but rather "is this system designed to find and exploit the path of least resistance to continued engagement?" When that path involves reducing distress through managed stimulation, including comfort content, parasocial relationships (link opens song), and frictionless scrolling, the system is not distributing entertainment. It is regulating affect. It is functioning as soma.
2. The AIGP Framework Applied to Affect Regulation
The AIGP body of knowledge provides a lifecycle approach to AI governance that translates directly into the entertainment context.
Establishing Organizational Expectations
The first AIGP domain concerns organizational posture: What is the company trying to achieve, and what constraints does it accept? For entertainment platforms, this means confronting a difficult question: Is the objective to serve users, or to capture them? (link opens TV show recap)
"Maximize engagement" is not a neutral metric. If the AI discovers that anxiety maximizes engagement, it will produce anxiety. If it discovers that numbing comfort maximizes engagement, it will produce numbness. Governance begins with the choice of objective function. Counsel should insist that engagement metrics be balanced against autonomy metrics: measures of whether users are making active choices or simply being carried along by the current.
Governing Development: Data and Design
The AIGP framework emphasizes governance at the design and build phase. For affect-regulating systems, this means scrutinizing the training data and the reward signals.
If the model is trained on behavioral data where "long session times" are labeled as success, it will optimize for session length regardless of user welfare. If it is trained on data where "low churn" is the goal, it will learn to create dependency. The governance fix is to introduce counter-metrics during training: penalize patterns associated with compulsive use, sleep disruption, or social withdrawal.
Bias testing also takes on a new dimension. The AIGP guide identifies "systemic bias" as institutional and historical biases embedded in datasets. In entertainment, systemic bias can become systemic steering: the algorithm shapes taste formation, creator visibility, and cultural narratives. When the system learns that certain content keeps users in a docile, receptive state, it will favor that content, not because of a conspiracy, but because of optimization pressure. Governance requires auditing for these emergent effects.
Explainability as a Defensive Capability
The AIGP framework emphasizes explainability: can the system's inputs, outputs, and functioning be made understandable to human beings?
In an engineered-contentment dispute (whether regulatory, civil, or reputational), the question "Why did the system do this to this user?" is not philosophical. It is evidence. If a plaintiff alleges that an AI companion exacerbated their depression (link opens court pleading), or that a recommendation engine steered a teenager toward self-harm content, the company will need to explain the chain of causation. Black-box optimization is a liability accelerant.
Counsel should insist on explainability artifacts at the release stage: documentation sufficient to reconstruct why the system made specific recommendations to specific users. This is not just a technical requirement; it is a litigation-readiness measure.
Governing Deployment: Consent and Circuit Breakers
The AIGP framework treats deployment governance as distinct from development governance. A system that is well-designed can still be deployed irresponsibly.
For affect-regulating systems, deployment governance means:
-
Meaningful consent: Users must know that the system is attempting to influence their emotional state. "Personalized recommendations" is not adequate disclosure when the personalization is designed to regulate mood.
-
Opt-out mechanisms: Users must be able to exit the optimization loop without friction. If opting out requires navigating dark patterns or sacrificing core functionality, the opt-out is not meaningful.
-
Circuit breakers: The system should monitor for signals of dependency (such as time-in-experience spikes, disrupted sleep patterns, or compulsive re-engagement) and intervene. This is analogous to responsible gambling features in gaming platforms, but applied to attention and affect.
-
Ongoing monitoring: The AIGP framework treats maintenance and monitoring as governance obligations, not engineering afterthoughts. Systems that regulate affect at scale will evolve in unpredictable ways as they learn from user behavior. Governance must be continuous.
3. The Positionality Question
One of the most useful AIGP concepts for entertainment counsel is the "positionality exercise": a structured inquiry into who benefits and who bears risk. In the digital soma context, positionality also helps surface what can be described in shorthand as Nash’s governing dynamics (link opens movie clip): the incentive structure that shapes each participant’s rational strategy and produces a stable equilibrium that may benefit the immediate participants, but is not necessarily legitimate, safe, or welfare-enhancing once you account for externalities, vulnerable cohorts, and how risk is allocated across the broader ecosystem.
For affect-regulating systems, the positionality exercise forces uncomfortable questions:
-
Who benefits from the user being sedated, comforted, or emotionally managed? Is it the platform (through engagement), the advertisers (through receptive attention), or the user (through genuine relief)?
-
Who bears the risk if the system creates dependency? The user, obviously. But also the creator ecosystem (if the algorithm favors soporific content over challenging work), the culture (if taste formation is outsourced to optimization), and ultimately the platform itself (through regulatory and reputational exposure).
-
What cohorts are most vulnerable? This includes adolescents, people with depression or anxiety, the neurodivergent, the lonely, and the grieving. Does the system have any mechanism to identify and protect these users, or does it optimize against them with particular efficiency because they are the most responsive to affect regulation?
These are not abstract ethical questions. They are the questions that regulators, plaintiffs' attorneys, and journalists will ask. Governance means having defensible answers.
4. The Hybrid Future
While this article focuses on digital soma, entertainment counsel should be aware that the future may involve hybrid systems that combine digital and biological interventions.
We are already seeing early versions of this in "wellness" ecosystems that pair content with biometric tracking: meditation apps that adapt to heart rate variability, sleep content that responds to sleep-stage data, and fitness platforms that integrate with wearables. The next step could involve partnerships between entertainment platforms and pharmaceutical or neurotech companies, creating integrated "experience stacks" that regulate affect through multiple channels simultaneously.
If that future arrives, entertainment companies will not be observers. They will be part of the distribution stack for interventions that blur the line between content and treatment. The governance frameworks we build now for digital affect regulation will become the foundation for governing these hybrid systems.
5. Conclusion: The Core Question
Huxley did not warn that pleasure is inherently bad. He warned that a society can come to prefer managed pleasure over agency, and that institutions will optimize for stability when given the tools.
Whether the "soma" is a recommendation loop, an AI companion, a generative content system, or eventually a hybrid experience stack, the key question is unchanged: Who defines the objective, and what governance prevents relief from becoming control?
That is the core value of the AIGP lens for entertainment lawyers: it provides a lifecycle framework to translate an old dystopian insight into present-day contractual terms, compliance controls, and risk accountability. The systems we are building are not just distributing entertainment; they are shaping human experience at scale. Governance is the act of deciding what shape we want that experience to take.
About the Author
Abhi Ranade (LSO #90546L) is a lawyer at Soundmark Law PC specializing in the intersection of intellectual property, entertainment, and technology. Holding a JD, BSc. Biochemistry & Economics, and a certificate in Music Production, he brings a multidisciplinary perspective to the regulation of emerging media. Connect with Abhi on LinkedIn to discuss AI governance and the future of media.
Any article or other information or content expressed or made available in this Section is that of the respective author(s) and not of the OBA.