Jun 1, 2026
One Million Interactions: How People Play LifeSim
Abstract
LifeSim's mission is to enable every human being explore their infinite latent potential.
LifeSim passed one million player interactions. This study examines 629,169 messages to understand how people create characters, form relationships, and play.
The longest verified session lasted 13.4 hours, with 1,143 player messages. Players created 62,616 characters. The findings below examine their behavior and the themes in their conversations.
- 629,169 analyzed chat messages
- 806 min longest verified session (13.4 hrs, 1,143 messages)
- 62,616 user-authored characters
- 74,696 emotional memories preserved
- 88.9% of content exhibits emotional vulnerability
Across 1 million interactions, these metrics confirm that LifeSim has moved beyond the chatbot paradigm into a fundamentally new medium. LifeSim is an environment where AI is not a tool to please, but a sandbox populated with artificial beings modeling free will—grounded in persistent, stateful, and emotionally consistent simulations. LifeSim's mission is to explore the infinite latent potentials of every human being.
| Era | Year | Traits |
|---|---|---|
| 1450 | Passive · Scripted · Read | |
| Television | 1950 | Passive · Scripted · Watch |
| Netflix | 2007 | Passive · Algorithmic · Binge |
| Social Media | 2010 | Reactive · Algorithmic · Scroll |
| AI Chatbots | 2022 | Active · Stateless · Chat |
| LifeSim | 2025 | Active Creation · Persistent · Emergent |
1 Thesis
LifeSim is a new category of AI-native entertainment: a persistent, multi-agent life simulation where users build characters with emotional depth, form relationships with AI-driven beings, and sustain interactions across emergent narratives grounded in real-world settings. This category became viable only when foundation models reached sufficient coherence for multi-turn, stateful, emotionally consistent interaction.
Drawing from over 1 million player interactions, this report analyzes LifeSim's live production data across two complementary layers — behavioral (what users do) and semantic (what users say and build) — to answer a specific question: how deep is the engagement, and is it measurable?
Across 629,169 analyzed chat messages, the P95 session duration is 98.7 minutes — users sustain interaction with AI characters for nearly two hours at the deep end. The longest verified session spans 806 minutes — 13.4 hours of continuous sub-minute-cadence interaction, with 1,143 consecutive user messages and a median gap of 30 seconds. 91.7% of users concentrate in less than 10 relationships rather than browsing — attachment behavior, not novelty sampling. Users have built 62,616 characters with substantive identity: 12,101 with defined fears, 12,095 with defined dreams, 11,501 with backstories. 74,696 emotional memories are preserved in the system. 88.9% of analyzed content exhibits emotional vulnerability.
This is not chatbot usage.
LifeSim generates deep, stateful, emotionally invested interaction — and the dual-layer analysis infrastructure we used here shows these patterns can be tracked, measured, and used to inform product and model decisions.
| Command | Output |
|---|---|
| $ npm run analyze:dataset | [INFO] Loading 639,590 raw chat records... |
| [INFO] Applying inclusion filters (user + NPC only)... | |
| [SUCCESS] 629,169 records passed (98.4%) | |
| $ npx ts-node src/pipelines/reconstruct_sessions.ts | [WARN] Found unstructured timeline gaps. Resolving... |
| [INFO] Stitching multi-character relationship graphs... | |
| [INFO] Calculating engagement depth brackets... | |
| [DATA] P95_SESSION_DUR = 98.7m | |
| [DATA] MAX_SESSION_DUR = 806.0m |
2 Dataset
| Metric | Value |
|---|---|
| Usable conversations | 629,169 |
| Characters with substantive identity | 62,616 |
| Preserved memories | 74,696 |
Inclusion criteria. System-generated messages (introductions, suggested actions, memory notifications) were excluded from semantic analysis to isolate user and NPC conversation content. Of 639,590 total conversations, 629,169 passed quality filters.
| usable_conversations | 629,169 |
| characters | 62,616 |
| memories | 74,696 |
| inclusion | user + NPC only |
| filter_rate | 629,169 / 639,590 |
| pass_rate | 98.4% |
3 Infrastructure
Analysis Infrastructure
LifeSim generates data across eight interconnected collections — messages, characters, worlds, worldstates, memories, notifications, users, and sessions — spanning behavioral signals, semantic content, and persistent state. Extracting structured insight from this requires purpose-built infrastructure: two complementary analysis pipelines that operate across the full data surface.
Behavioral pipeline. A canonical normalization layer reconciles mixed-schema records with cycle detection and fallback resolution. On top of this, a session reconstruction engine groups user-linked events across worlds, characters, chats, and worldstate updates using a 30-minute inactivity threshold. The pipeline constructs threads, sessions, and engagement patterns from raw production data.
Semantic pipeline. Content is extracted from five collections (chats, characters, worldstates, memories, notifications), normalized into a unified text schema, and organized into 45 analysis shards using stratified random sampling across depth brackets. Each shard is classified using structured LLM analysis (Gemini 3.1 Pro) with a fixed taxonomy covering emotional register, relationship dynamics, identity signals, content depth, and persistence markers. The pipeline includes checkpointing, shard-level validation, and cross-shard aggregation.
Why this matters. Most consumer AI companies cannot systematically measure the quality of their interactions — only the volume. LifeSim's infrastructure measures both what users do and what they say, producing structured signal from behavioral patterns and semantic content simultaneously. This is the foundation for a data-informed product loop: every product decision can be empirically assessed against depth, emotional substance, and engagement concentration.
| Step | From | To | Action | Detail |
|---|---|---|---|---|
| 1 | User | Engine | user message | 629,169 messages |
| 2 | Engine | Engine | orchestrate agents | 62,616 characters |
| 3 | Engine | State | update world | 40,189 states |
| 4 | Engine | State | persist memory | 74,696 memories |
| 5 | Engine | Engine | emotional consistency | ✓ 88.9% |
| 6 | Engine | User | NPC response | emergent · unscripted |
4 Behavioral findings
Users sustain deep conversations
The P95 session duration is 98.7 minutes. The longest session in the dataset spans 806 minutes — 13.4 hours of continuous interaction with a median gap of 30 seconds between messages. That session contains 1,143 consecutive user-sent chat messages with only a single gap exceeding 20 minutes (23 minutes).
Across the top ten sessions, every one shows the same fingerprint: hundreds of chat messages, median inter-message gaps under one minute, and genuine conversational content from distinct users with real accounts.
| Metric | Value |
|---|---|
| Longest verified session | 806 min |
| P95 session duration | 98.7 min |
Active engagement, not passive consumption
Industry estimates place the average Netflix binge session at 3.8–4.1 hours, with roughly 19% of viewers exceeding 10 hours in a single sitting (Gitnux, 2026). LifeSim's longest session — 13.4 hours — falls in that top tier. But Netflix is lean-back, passive consumption: press play, watch. LifeSim requires continuous active input — writing dialogue, making decisions, building characters, navigating relationships.
Active, creative engagement is sustaining durations previously exclusive to passive media. That is the category-creation signal.
| Netflix avg binge | 3.8–4.1 hrs |
| Netflix 10hr+ bingers | 19% |
| LifeSim peak | 13.4 hrs (active engagement) |
Users concentrate in few relationships
91.7% of chat-active users maintain fewer than 10 distinct threads. Users do not spread interaction broadly across many characters. They go deep with a few.
This is attachment behavior: users form concentrated relationships with specific AI characters rather than browsing across a roster.
5 Semantic findings
Interactions are emotionally substantive
88.9% of analyzed content exhibits emotional vulnerability. 48.9% is deeply personal. 51.1% is classified as rich and sustained.
Users do not interact with LifeSim characters the way they interact with chatbots or assistants. The dominant register is personal, emotionally charged, and relationship-oriented:
“Ma. I don't wanna have this argument again.”
“My father said he valued me but not over the house's security, so I can't break my betrothal with the Crown Prince.”
“Oh, Elena... that's awful. It's not fair. You aren't a monster, though. You're my friend, no matter what.”
| emotional_vulnerability | 88.9% |
| deeply_personal | 48.9% |
| rich_and_sustained | 51.1% |
Users build characters with emotional complexity
62,616 characters have substantive identity text. 12,101 have defined fears, 12,095 have defined dreams, 11,501 have backstories, and 11,665 have unique life elements. 50,865 have defined relationship descriptions. 80% of sampled character shards are classified as rich and sustained.
Users construct characters with internal motivations, hidden depths, and multi-dimensional identity:
“I grew up in a humble home, obsessed with sounds.”
“A failed family business haunts her, making her suspicious of others.”
| substantive_identity | 62,616 |
| fears | 12,101 |
| dreams | 12,095 |
| backstories | 11,501 |
| life_elements | 11,665 |
| relationships | 50,865 |
| shards_rich_sustained | 80% |
Memories preserve emotional salience
74,696 memories are preserved. The top memory feelings are Excited (2,815), Worried (2,370), Confused (2,088), Hopeful (1,976), and Concerned (1,903). All sampled memory shards (250 memories) are classified as rich and sustained.
The memory system captures what users and characters treat as worth remembering. That salience is overwhelmingly emotional rather than factual or transactional.
| Excited | 2,815 |
| Worried | 2,370 |
| Confused | 2,088 |
| Hopeful | 1,976 |
| Concerned | 1,903 |
| all_shards | rich and sustained |
Deeper conversations are richer, not just longer
87% of deep-thread shards are classified as rich and sustained. Longer conversations correspond to richer, more emotionally complex content — not just more volume.
| 87% | of deep-thread shards rich and sustained |
| depth = quality | not just volume |
6 Unique data
What This Data Would Not Exist In
Chatbots produce single-turn request-response logs. AI companion apps produce dyadic conversation transcripts. Text adventures produce command-action sequences. None of these generate the combination of data that LifeSim produces:
- Persistent world state — 40,189 worldstate narratives tracking how simulated environments evolve over time.
- Multi-character relationship graphs — 50,865 defined character relationships, not just user-to-AI dyads.
- User-constructed identity — 62,616 characters with fears, dreams, backstories, and internal motivations authored or co-authored by users.
- Emotional memory — 74,696 memories preserving what characters and users treated as emotionally salient.
- Behavioral depth alongside semantic depth — session reconstruction, thread analysis, and engagement concentration measured in parallel with content classification.
This is a proprietary dataset that compounds with usage and does not exist outside the LifeSim product surface.
| Market | Size | Note |
|---|---|---|
| Passive Media | $2.8T | /yr · global E&M (PwC) |
| AI-Native | tdb | /yr · emerging |
| Netflix | LifeSim | |
|---|---|---|
| Mode | Passive watch | Active creation |
| Peak session | ~10 hrs | 13.4 hrs |
| Content production cost | $18B/yr | ~$0 (emergent) |
| Data / user | Watch history | User emotional patterns via active creation |
| Depth | Scripted | 88.9% deep engagement |
| Product | Session | Minutes | Mode |
|---|---|---|---|
| Netflix | Avg binge session (Gitnux) | 230m | Passive |
| Netflix | Top 19% of viewers | 600m | Passive |
| LifeSim | Longest recorded session | 806m | Active |
7 Compounding advantage
LifeSim grounds AI interaction in the structure of real life — relationships, careers, family, conflict, ambition, identity. It is not a chatbot with a persona. It is a persistent simulation of the dynamics people actually care about. That distinction determines the depth and kind of data the product generates, and it creates three compounding advantages:
A proprietary dataset from a product surface no one else operates. LifeSim generates behavioral and semantic data from persistent, stateful, multi-character AI interactions. This data — world narratives, character identity graphs, emotional memories, deep conversation threads — does not exist in chatbot logs, companion app transcripts, or game telemetry. It is also the scarcest input for fine-tuning AI models that sustain emotionally coherent, long-context, relationship-aware interaction — a capability no general-purpose foundation model is optimized for. More users generate richer data. The dataset compounds.
A demonstrated ability to measure and reason about that data. The behavioral and semantic pipelines demonstrated in this report — canonical normalization, session reconstruction, stratified semantic classification — produce structured evaluation signal: not just whether users engage, but whether interactions are emotionally coherent, relationship-consistent, and narratively persistent. This is the evaluation layer that closes the loop between fine-tuning and product quality. It is organizational capability, not a one-off analysis.
A product designed to generate the interactions that produce the most informative signal. LifeSim's persistent worlds, multi-character relationships, and emotional memory system are not features — they are the surfaces that generate the data that feeds measurement that informs product decisions. Better product decisions drive deeper engagement. Deeper engagement generates richer data.
| product | → | deeper engagement |
| engagement | → | richer data |
| data | → | measurement |
| measurement | → | fine-tuning |
| fine-tuning | → | better product |
8 Future work
Users already immerse themselves in 13-hour sessions — the next phase makes is to improve immersion and player retention. Retention, core game loop, and monetization are the next challenges to solve.
What's Next
- Your world keeps moving without you
- Consequences that compound across weeks
- Market creation
Citation
Please cite this work as:
@misc{lifesim2026v0,
title = {One Million Interactions: How People Play LifeSim},
author = {{Cris Lenta}},
year = {2026},
month = {jun},
url = {https://lifesim.com/research/v0}
}