1. Introduction: The Missing Layer of AI

Artificial intelligence has advanced rapidly over the past decade. Large language models can generate essays and code. Vision systems can identify faces and objects. Reinforcement learning systems can beat world champions in Go and StarCraft. Yet all of these advances share the same blind spot: they lack emotional intelligence.

Emotion is not an optional dimension of human life. It is the organizing force behind decision making, memory, motivation, and social connection. It shapes how we learn, how we recover, and how we act in moments of crisis. Without emotion, intelligence is incomplete.

Today’s AI systems attempt to reduce emotion to simple states. They categorize a voice clip as “happy,” a photo as “angry,” or a sentence as “sad.” These are snapshots, stripped of time and stripped of context. But emotions are not static. They emerge, intensify, plateau, and resolve. They interact with our bodies, our words, and our environments.

Ignoring this temporal and multimodal nature of emotion results in shallow models. These models may work in a demo, but they collapse in real-world use. Just as natural language processing required the transformer architecture to understand words in sequence, emotion modeling requires an architecture that treats feelings as processes, not labels.

Psyche exists to build the missing foundation. Our goal is to create the first foundational model for emotion: a system that can learn the dynamics of emotional state across multiple inputs and anticipate how emotions evolve over time.

We believe this is the next frontier of AI.

2. Why Emotion Matters

Emotion matters because it governs the core aspects of human experience. It impacts health, productivity, trust, and the very economy we live in.

Human health
Mental health challenges are widespread. The World Health Organization estimates that depression and anxiety cost the global economy nearly one trillion dollars in lost productivity each year. Burnout is now recognized as an occupational phenomenon. Despite this, our healthcare systems remain reactive. We treat crises after they happen rather than identifying precursors.

Fitness apps count steps and calories. Smartwatches measure sleep. Yet none of these systems capture the emotional dimension. By the time a person receives clinical attention for depression or anxiety, the condition may already be severe. Psyche aims to make emotion visible and measurable so that interventions can happen earlier.

Human-machine trust
Robots and AI assistants are moving into hospitals, homes, and workplaces. Without the ability to understand emotional cues, they feel unnatural. A robot that cannot recognize frustration will frustrate its users. A voice assistant that cannot distinguish between calm and distress cannot be trusted in emergencies. Emotional intelligence is essential for safe and effective adoption.

The attention economy
The dominant business model of the internet has been to exploit emotions. Platforms amplify outrage and anxiety to maximize engagement. This has created measurable harm to individuals and societies. The next generation of systems must move in the opposite direction. They must empower users to regulate emotions, recover faster, and find balance. Psyche’s goal is to flip the paradigm: from extraction to empowerment.

Emotion is not secondary. It is central. The systems that can model it will unlock new forms of value across health, work, and society.

3. Defining Emotion as Data

To build a foundational model for emotion, we must first define what emotion looks like as data.

Physiological signals
Our nervous systems constantly generate signals that reflect emotional state. Heart rate variability (HRV) is a well-established marker of stress and recovery. Sleep cycles and disruptions reveal fatigue and resilience. Movement patterns correlate with mood and energy. These are measurable, objective signals collected by consumer wearables like Apple Watch, Oura, and Whoop.

Behavioral signals
Emotion also reveals itself in behavior. The tone of a person’s voice, the rhythm of their speech, and their facial micro-expressions provide subtle cues. Language choice matters too: pronoun use, sentiment, and changes in expression all reflect internal state.

Temporal patterns
Most importantly, emotion is not static. A stressful event spikes HRV, but recovery can take hours or days. Sleep deprivation accumulates. Joy builds and spreads through social contact. Emotions must be studied as trajectories.

A taxonomy of inference
We propose three levels of emotional inference:

Directly measurable: signals with clear, validated links (e.g., HRV to stress).

Strong inference: multimodal signals that combine to form a confident interpretation (e.g., tired voice + poor sleep data → fatigue).

Weak inference: ambiguous signals that may be suggestive but not reliable in isolation.

By separating these levels, we avoid the trap of overclaiming. Psyche builds on what is directly measurable while expanding inference with caution and transparency.

4. The Challenge of Temporal Modeling

Most emotion AI today works like a snapshot. It labels a facial expression or a sentence. This misses the most important part: change over time.

Emotions evolve. Stress rises across a workday, peaks in the afternoon, and recedes with rest. Anxiety builds during uncertainty and resolves when outcomes become clear. Recovery from sadness takes longer than recovery from anger. These dynamics matter more than static labels.

The challenge is that temporal modeling requires large, continuous datasets. A single snapshot is easy to label. A multi-day emotional trajectory is harder to capture. Yet this is the level of modeling required for real-world use.

Advances in transformers and self-supervised learning create the opportunity. Just as language models learned the structure of sentences from raw text, emotion models can learn the structure of emotional processes from raw physiological and behavioral data.

Psyche’s approach is to align multimodal streams across time and train models to anticipate trajectories. The goal is not just to identify an emotion but to predict its course.

5. Purpose-built Multimodal Architecture

Emotion lives in multiple signals at once. A purpose-built architecture must bring them together.

Biometric inputs
Wearables capture HRV, sleep, and movement. These are strong indicators of stress, recovery, and fatigue.

Voice analysis
Acoustic models can detect shifts in tone, rhythm, and energy. A flat tone may suggest fatigue. Rapid speech may reflect anxiety.

Language understanding
Large language models can detect shifts in sentiment and framing. The words “I’ll try” carry different weight than “I will.” Subtle shifts in pronouns or negations can signal emotional change.

Visual cues
Facial micro-expressions, posture, and gestures contribute additional layers of context. These signals are powerful but require careful ethical consideration.

Temporal alignment
The key is aligning these modalities within a temporal framework. Emotion is not the sum of static snapshots. It is the pattern of signals over time. Psyche’s architecture is designed to unify these inputs into a dynamic model.

Ethics-first design
We prioritize opt-in participation and transparent use of data. Individuals own their emotional signals. In the future, biometric royalties may compensate people for contributing to emotional AI systems.

This is not another consumer app. It is infrastructure for the next era of AI.

6. Early Proof Points

Big visions require tangible demonstrations. Psyche is launching early proof points that show the feasibility of emotional intelligence in action.

Smart Home Demo
We are installing an immersive demo unit in an apartment building in Pittsburgh, PA. Voice tone and biometric data will drive real-time adaptation of lights, sound, scent, and climate. Visitors will experience what it feels like when a space responds to emotion.

Consumer Companion App
We are developing a companion app that interprets Apple Health and wearable data. The app translates raw physiological signals into daily emotional insights. Users can track patterns and reflect on trajectories.

Pilot Integrations
Working with Philips Hue, Sonos and Pura can show how emotional intelligence can orchestrate environments already in millions of homes. Lights dim when stress is high, music adjusts to support recovery, and scent diffuses to promote calm and grounding, turning the environment into an ally for emotional regulation.

These are not endpoints. They are doorways into the larger vision.

7. Setting New Benchmarks for Emotional AI

Evaluation matters. Current benchmarks for emotion AI are flawed because they focus on static classification. Psyche proposes a higher standard.

Support axis
We classify signals as: directly measurable, strongly inferred, weakly inferred, or unsupported.

Severity axis
We rate the impact of errors as minimal, moderate, or major. Misclassifying stress recovery is moderate. Missing a signal of acute distress is major.

Example scenario
Transcript: “I barely slept, but I’ll push through today.”
Wearable data: low HRV, poor recovery.
Static model: neutral.
Psyche model: predicts rising burnout risk, prompting intervention.

This taxonomy creates transparency. Not all inferences are equal. Not all errors are equally severe. By defining standards, we set benchmarks for progress.

8. Vision for an Emotional Operating System

Psyche is not building a single product. We are building the emotional operating system of the future.

Homes will adapt to residents, becoming sanctuaries that regulate stress and promote recovery.

Robots and assistants will anticipate emotional context, making collaboration seamless.

Healthcare systems will shift from reactive treatment to preventive care, flagging emotional dysregulation before illness develops.

Workplaces will balance productivity with well-being, responding to collective mood.

Cities will sense emotional patterns at scale, enabling more empathetic policy and responsive services.

The same way electricity rewired daily life and the internet rewired communication, emotional intelligence will rewire how humans and machines coexist. Psyche intends to be the company that builds this infrastructure.

9. Conclusion

The world’s largest technology companies have mastered vision, language, and computation. Yet none have addressed the emotional dimension.

That is where Psyche begins.

Emotion is the last missing layer of intelligence. The company that models it will not just build an app. It will define the architecture of human-machine symbiosis for the century ahead.