The End of an Era: Why OpenSeriousGame is Becoming Obsolete and What Comes Next
A critical retrospective and forward-looking analysis from the movement’s founder
The Uncomfortable Truth: OSG’s Structural Limitations 🤔💭
After 7 years building the OpenSeriousGame movement, reaching thousands of learners, and creating a viral knowledge transmission ecosystem, I must confront an uncomfortable reality: OpenSeriousGame will indeed become obsolete and outdated in its current form. 📉⚰️
This isn’t a failure—it was useful for several years and accomplished its mission. But the current format is structurally and systematically limited for the class of problems we face in the AI era. 🤖⚡
The Founder’s Paradox: Death and Rebirth 🔄✨
As the founder of this movement, acknowledging OSG’s obsolescence is both emotionally challenging and intellectually exhilarating. 😅💪 It’s hard to watch your creation reach its natural limits, but it’s equally exciting to architect the next technological layer that will surpass what we thought possible.
Right now, as I write this on June 9, 2025, I’m actively preparing the markdown conversion of OpenSeriousGame elements 📝🔧—the first step in this evolutionary transition. This isn’t an ending; it’s a metamorphosis. 🐛➡️🦋
Why OSG Worked (And Why That’s Now Its Weakness)
The Pre-AI Era Success Formula 🎯✨
OpenSeriousGame succeeded because it solved specific problems of the 2018-2022 era:
Heavy Meeting Dependency: Live workshops created deep transformation through full-bandwidth human interaction—movement, emotion, presence—that screen-based activities couldn’t replicate. 👥💪
Manual Expertise Transmission: OSG enabled passionate individuals to transform their expertise into transmissible games through systematic documentation and role progression. 📚🎮
Community-Driven Quality: Peer networks maintained standards while enabling distributed creation and improvement. 🌐🤝
Viral Knowledge Propagation: Each participant could become a facilitator, creating exponential rather than linear scaling. 📈🦠
The Structural Bottlenecks 🚧⚠️
These same strengths have become systematic limitations:
1. Geographic and Temporal Constraints 🌍⏰
- Dense cities required for effective meetups
- Physical presence dependency limits global scaling
- COVID demonstrated fragility of purely in-person models 😷
2. Language and Cultural Barriers 🗣️🌏
- Mostly French content restricts international expansion
- Manual translation requirements for each game
- Cultural adaptation needed for different organizational contexts
3. Technical Skill Gaps 🔧❌
- Many potential propagators lack game design capabilities
- Manual creation process energy-intensive and slow ⚡🐌
- Quality depends heavily on individual talents mixed in “fourre-tout” fashion
4. Limited Contextual Adaptation 🎯📋
- Static games poorly adapted to dynamic organizational needs
- High exploration costs in game databases 💸
- Manual adaptation required for specific contexts
- Long execution delays (printing, learning, preparing, scheduling) ⏳
The AI-Era Context Shift 🤖🌊
From Scarcity to Abundance 📈💎
We’ve moved from information scarcity (where OSG’s open-source sharing was revolutionary) to intelligence abundance where the bottleneck isn’t content creation but contextual relevance and dynamic adaptation. 🎯⚡
From Manual to Automated 🔧🤖
The current OSG model treats game design like “Web 1.0 with static content”—requiring manual operators and front-end code reuse. We lack:
- Standardized game qualification systems ❌📋
- Modular construction elements beyond basic tools like Mécanicartes 🧩
- Factory-systematized game production 🏭
- Dynamic gameplay adaptation 🔄
From Human-Dependent to Human-Augmented 👤➕🤖
Current facilitation requires significant human expertise for:
- Context assessment and need qualification 🎯
- Game selection and adaptation 🎮
- Session flow management and real-time adjustments ⚡
- Post-session integration and follow-up 📊
The Neuro-Symbolic Architecture for Dynamic Game Generation 🧠🎮⚡
Core Innovation: Game Objects as Code 💻🧩
The next generation requires treating serious games like software systems with standardized components:
Game Design Objects: Gameplay elements, artistic components, learning mechanics 🎨🎯 Quality Control Systems: Coherence checklists, semantic validation, user requirement testing ✅🔍 Context Integration: Organizational constraints, cultural adaptation, technical requirements 🏢🌍 Transmission Frameworks: Built-in viral propagation and skill transfer mechanisms 🦠📈
Dynamic Constraint-Based Generation ⚡🎯
Instead of selecting from existing games, the system would generate games dynamically to meet specific constraints:
Context Assessment → Constraint Definition → Dynamic Generation → Quality Validation → Iteration
Input Requirements: 📋
- User/learner needs analysis (like JAVAD tree game) 🌳
- Facilitator requirements (ideally minimal for scalability) 🎤
- Organizational context (like #OpenSeriousFilters) 🏢
- Transmission requirements (existing OSG guide framework) 📚
- Cultural gaming preferences (pre-survey integration) 🎮🌍
Implementation Architecture 🏗️🚀
Phase 1: Data Migration 📦➡️💻
- Convert existing OSG catalog to markdown format
- Migrate to GitHub for AI accessibility and platform independence
- Establish modular component library 🧩
- Create coherence validation frameworks ✅
Phase 2: Generation Engine ⚙️✨
- Iterative draft generation with choice explanations
- Visual/non-text representations for validation 🎨
- Human-AI collaborative validation cycles 👥🤖
- Live session format emphasis with online authority content mapping 🌐
Phase 3: Dynamic Facilitation 🎭⚡
- Real-time gameplay adaptation based on participant responses
- Dynamic session flow like advanced facilitators naturally do 🔄
- Contextual content insertion based on emerging needs 🎯
- Automated difficulty and engagement adjustment 📊
Neuro-Symbolic Integration 🧠⚡🔗
This architecture leverages neuro-symbolic AI that combines:
Neural Networks: Pattern recognition in participant behavior, context analysis, game element effectiveness 🧠📊 Symbolic Reasoning: Rule-based game design principles, logical constraint satisfaction, explicit requirement validation ⚖️🎯
This hybrid approach enables both learning from data and maintaining logical consistency—essential for educational game design. 🎓✨
The Video Game Solution: Professional Design at Scale 🎮🎯⚡
Automatic Soft-Skills Mapping 🗺️🧠
AI can now map soft-skills development needs to existing video games automatically. Modern video games offer:
- Incredibly advanced design, gameplay, and artwork 🎨✨
- Proven engagement and flow mechanics ⚡🎯
- Rich behavioral learning opportunities 🧠💪
- Massive existing content libraries 📚🎮
What was missing: Quality mapping systems to find appropriate games and transform gameplay lessons into workplace behavioral applications. 🔗💼
UI Layer for Context Qualification 📱🎯
Future systems could provide better qualification interfaces for pedagogical contexts, mapping to relevant games and rendering obsolete the amateur game design of professionals whose core expertise lies elsewhere (agilists, coaches, trainers). 🚀
Value Chain Separation: 🔗
- Agilists/Coaches: Expert in context qualification and behavior change mandate 🎯👥
- Professional Game Designers: Expert in engagement mechanics and learning design 🎮🎨
- AI System: Expert in mapping between contexts and appropriate game solutions 🤖🗺️
Implementation Pathway: From OSG to AI-Generated Experiences 🛤️🚀
Stage 1: Hybrid Transition 🔄⚖️
- Maintain current OSG for “backup low-tech” scenarios 🔧⚡
- Begin markdown conversion and GitHub migration 📝➡️💻
- Develop constraint qualification systems 🎯📋
- Create component standardization frameworks 🧩✅
Stage 2: Generation System Development ⚙️🚀
- Build AI-assisted game generation tools 🤖🎮
- Integrate real-time context adaptation ⚡🎯
- Develop quality validation systems ✅🔍
- Test dynamic facilitation capabilities 🎭📊
Stage 3: Full Ecosystem Replacement 🌐🔄
- Deploy neuro-symbolic generation engines 🧠⚡
- Enable real-time contextual adaptation 🎯⚡
- Integrate with existing organizational systems 🏢🔗
- Maintain human oversight and improvement feedback 👥📈
The Cultural and Social Dimension
Preserving Human Connection 💝🤝
OSG will retain value as “backup low-tech” similar to how:
- Books didn’t disappear with screens 📚💻
- Basic cooking tools survived the Thermomix era 🔪🤖
- Manual processes complement automation ✋⚙️
Continued Value: Defending “a pleasant image of humanity that enjoys itself, learns together, has good times, shares information and helps each other develop with serendipity.” 😊🌟👥
Not Dead, Just Mature Technology ⚰️➡️🧓
OSG transitions from cutting-edge innovation to mature technology that works but is slow relative to AI-enhanced alternatives. ⚡🐌 Like many useful technologies, it becomes a specialized tool for specific contexts rather than the primary solution. 🔧🎯
Strategic Implications for Organizations
For Learning & Development Leaders
- Begin experimenting with AI-assisted content generation
- Develop capabilities in prompt engineering and context qualification
- Maintain human facilitators for complex interventions
- Invest in hybrid systems that combine automation with human insight
For Community Builders
- Focus on relationship facilitation rather than content creation
- Develop skills in AI system integration and optimization
- Emphasize unique human value: empathy, cultural navigation, complex problem-solving
- Build bridges between AI-generated content and human community needs
For Game Designers and Facilitators
- Evolve toward AI collaboration rather than competition
- Specialize in complex, high-touch interventions
- Develop expertise in AI system training and refinement
- Focus on meta-design: designing systems that design experiences
The Broader Technological Context
MCP Integration Potential
With markdown standardization, Model Context Protocol (MCP) integration becomes possible, enabling:
- Dynamic content sharing between AI systems
- Persistent learning from community feedback
- Cross-platform game generation and adaptation
- Distributed intelligence coordination
Connection to Emerging AI Capabilities
This evolution aligns with broader AI developments:
- Neuro-symbolic AI: Combining learning and reasoning for better game design
- Agent-based systems: Autonomous facilitation and content adaptation
- Dynamic content generation: Real-time customization for specific contexts
- Human-AI collaboration: Augmenting rather than replacing human facilitators
Call to Action: Building the Future
For Current OSG Community
- Help migrate existing content to markdown/GitHub format
- Participate in developing game object standardization
- Experiment with AI-assisted game generation tools
- Document what makes great facilitation for AI training
For Organizations
- Begin experimenting with AI-enhanced learning design
- Develop internal capabilities for context qualification
- Invest in systems that combine AI generation with human oversight
- Prepare for transition from manual to automated learning content creation
For AI and EdTech Developers
- Build neuro-symbolic systems for educational content generation
- Develop better context qualification interfaces
- Create quality validation systems for AI-generated learning experiences
- Focus on human-AI collaboration rather than replacement
The Philosophy of Productive Obsolescence
This analysis embodies a key principle of systems thinking: genuine innovation eventually makes itself obsolete by solving the problems it was designed to address.
OSG succeeded in awakening transmission abilities and proving viral knowledge propagation was possible. Now, more sophisticated systems can automate and scale these capabilities beyond what manual processes could achieve.
The goal was never to preserve OSG indefinitely, but to solve the underlying problem: enabling widespread, effective knowledge transmission.
In the AI era, we can pursue this mission through more powerful tools while preserving the human connection and joy that made OSG valuable in the first place.
The future belongs to human-AI collaborative intelligence where artificial systems handle routine generation and adaptation while humans focus on wisdom, empathy, and the irreplaceable elements of transformative learning experiences.
Questions for your context:
- How might AI-generated learning experiences enhance rather than replace human connection in your organization?
- What aspects of manual content creation could be automated without losing pedagogical effectiveness?
- How can we preserve the joy and community aspects of learning while leveraging AI capabilities?
The game is changing. The question is: how will you evolve to play it?
This retrospective analysis comes from 7 years building the OSG movement and witnessing its impact across 100,000+ learners. The future lies not in preserving what worked, but in evolving toward what works better.