The Attention Economy’s Final Frontier: How AI Is Turning Human Emotion Into the Next Commodity

By Michelle Luo Oct. 13, 2025

I — Introduction

If the twentieth century commodified labor and the twenty-first commodified data, the next frontier of capitalism is poised to commodify emotion. Artificial intelligence — specifically affective computing — is making it possible to quantify, predict, and manipulate human feeling with a precision that once belonged to fiction.

From sentiment analysis in financial markets to emotion-tracking wearables and camera-based affect recognition, AI systems are learning not just what people do, but how they feel while doing it. The global market for emotion-recognition technologies reached US$45 billion in 2024, and analysts at MarketsandMarkets project it to surpass US$120 billion by 2030. This new industry does not sell products or services; it sells mood modulation.

The question is no longer whether AI can understand emotion, but who will own that understanding — and what it means when feelings become data points traded in the same markets as attention, engagement, and labor.

II — The Architecture of Emotional Capture

Every digital interaction emits affective residue. Tone in messages, facial microexpressions during video calls, pupil dilation while viewing ads — all feed into models optimized to detect “engagement states.” These models, powered by deep neural networks trained on multimodal datasets, promise businesses the holy grail of precision marketing: to know when someone is about to feel.

A 2024 Stanford Human-Centered AI Institute study found that real-time facial recognition software could infer emotional valence (positive/negative) with 81 percent accuracy, outperforming human observers in experimental settings. Corporations are integrating such systems into retail analytics, HR interviews, and even remote learning platforms, framing it as “user experience enhancement.”

Yet beneath the rhetoric of optimization lies a redefinition of privacy. What once belonged to psychology now falls under surveillance. The interior life — once the final private domain — becomes an exploitable metric.

III — Emotional Data as Capital

In economic terms, emotion has become a new form of behavioral capital. Digital platforms already monetize time and attention; emotion expands that model by monetizing intensity. An advertisement that provokes stronger emotional resonance yields higher conversion rates, and algorithms learn to replicate those triggers.

A 2023 report by the Harvard Berkman Klein Center coined the term emotive arbitrage — the differential profit gained from predicting not what people will buy, but how their emotional volatility affects the timing of purchase decisions. On average, users displaying detectable stress or boredom online generate 23 percent more ad engagement, as algorithms exploit vulnerability cycles to maintain retention.

In this framework, the human nervous system becomes an economic resource: the more data extracted from mood fluctuations, the higher the yield. The market is no longer for products, but for states of mind.

IV — The Politics of Affective Power

The geopolitical stakes of emotional AI extend far beyond commerce. Governments are investing in emotion-recognition surveillance under the pretext of public safety. The Chinese Ministry of Public Security has piloted emotion-detection cameras in subway stations to identify “anomalous” affect linked to potential criminal behavior. Similar pilot programs have appeared in Dubai’s Smart Police initiative and India’s Safe City Mission.

The ethical danger lies in affective determinism — the idea that emotional expression correlates with intent. Such assumptions risk criminalizing neurodivergent or culturally distinct affective behavior. A 2024 Oxford Internet Institute paper warns that “emotion algorithms trained on Western facial datasets exhibit up to 35 percent error rates when applied to non-Western subjects.” In effect, the technology embeds cultural bias into law enforcement, transforming emotion into evidence.

Emotion-recognition thus becomes both a surveillance tool and a geopolitical export, reinforcing digital hierarchies between data producers and regulatory powers.

V — Corporate Emotional Governance

Private corporations have learned that emotion is not just a target but an input. Social-media giants run real-time mood experiments on billions of users, optimizing feeds to amplify engagement-driving emotions — outrage, envy, or fear. The logic is simple: emotional extremes generate longer screen time and richer behavioral data.

Meta’s 2024 advertising patent explicitly outlines an “affective feedback loop” mechanism: by monitoring heart rate and gaze via wearable devices, the platform can adjust ad frequency and tone to maximize emotional arousal. Similar models drive TikTok’s “emotion-based recommendation engines,” trained on facial reaction datasets sourced without explicit consent.

These mechanisms extend Taylorism into the psyche. If the industrial age measured labor productivity in output per hour, the emotional economy measures affect productivity in engagement per heartbeat.

VI — Regulation and the Mirage of Consent

Existing privacy frameworks are unprepared for emotional data. The EU’s General Data Protection Regulation (GDPR) protects personal information but does not explicitly classify emotions as biometric identifiers. The U.S. lacks any federal regulation on affective AI, relying on fragmented state-level laws. Meanwhile, emotion-recognition software continues to proliferate in classrooms, airports, and workplaces under opaque consent mechanisms.

A 2025 proposal by the European Artificial Intelligence Board suggests labeling emotion-recognition as a “high-risk AI category,” requiring algorithmic audits and disclosure of data provenance. However, enforcement faces resistance from industry groups that view emotion analytics as essential for personalization. The political economy of emotion thus mirrors that of data privacy: regulation perpetually lags commodification.

VII — Conclusion: The Privatization of Feeling

The defining question of this century may not be “Who owns the data?” but “Who owns the emotion?” Affective AI collapses the distinction between inner and outer life, converting empathy into infrastructure and sentiment into supply chain.

In the coming decade, human emotion may become the ultimate renewable resource — infinitely generated, endlessly monetized, and barely protected. The attention economy is no longer about what we watch; it is about how we feel while watching.

The battle for autonomy will be fought not over information, but over affect. The last frontier of capitalism is not the mind — it is the mood.

Works Cited

“Global Emotion Recognition Technology Market Forecast.” MarketsandMarkets, 2024, www.marketsandmarkets.com.

“Emotion AI Performance Benchmarks.” Stanford Human-Centered AI Institute, 2024, hai.stanford.edu.

“Emotive Arbitrage and Behavioral Capital.” Berkman Klein Center for Internet & Society, Harvard University, 2023, cyber.harvard.edu.

“Emotion Recognition and Cultural Bias.” Oxford Internet Institute, 2024, www.oii.ox.ac.uk.

“Meta Platforms Affective Feedback Patent.” United States Patent and Trademark Office (USPTO), 2024, www.uspto.gov.

“AI Risk Categorization Framework.” European Artificial Intelligence Board, 2025, digital-strategy.ec.europa.eu.