# **New Intelligence Report: Richard Sutton Joins ExperienceFlow.AI to Advance Experience-driven AI**
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## **Overview**
Over the past two years, AI has swept the world by **imitating humans** through generative models.
But Richard Sutton — the father of reinforcement learning — warns:
> “The GenAI era is coming to an end.”
Armed with the prestige of a 2024 **Turing Award**, Sutton has joined **ExperienceFlow.AI**, a little-known AI startup, with a bold new mission: to awaken AI through **experience** rather than human-fed datasets.
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## **Richard Sutton’s Return: Challenging the Generative AI Paradigm**
In early November, ExperienceFlow.AI’s CEO **Giri ATG** posted a concise announcement on X:
No flashy posters. No hype videos.
Yet it quickly spread through AI research and investment circles like a seismic signal.
Key facts:
- **Richard Sutton** — pioneer of reinforcement learning, author of *Reinforcement Learning: An Introduction*.
- Recipient of the **2024 Turing Award** alongside Andrew Barto.
- Known for enabling AI to *“learn from experience.”*
### **Why This Matters**
For two years, generative AI dominated tech headlines. Sutton’s silence during this period ended with his appointment as **Chief Science Officer** and the launch of a *Superintelligence Research Lab*.
His message is clear:
- **Generative AI** → learns from *human data*.
- **Reinforcement Learning** → learns from *interaction with the environment*.
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## **Defining True Intelligence: Learning by Doing**
Sutton draws a sharp contrast:
> “Generative AI depends on human datasets; we focus on creating new knowledge from experience.”
### **Reinforcement Learning Essentials**
- **Agent** acts in an **environment**.
- Receives **reward**.
- Adjusts **policy** based on feedback.
- Builds knowledge autonomously.
Sutton predicts that relying solely on human-fed data will:
- Limit AI to mimicking and speculating.
- Prevent genuine understanding.
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## **ExperienceFlow.AI: Building Experience-driven Decentralized Superintelligence**
### **Company Vision**
ExperienceFlow is headquartered in San Francisco and is focused on:
> **“Experience-driven decentralized super intelligence.”**
Giri ATG explains:
- Solve the last critical challenge toward **Artificial General Intelligence (AGI)**.
- Prioritize **true reinforcement learning** to drive:
1. Continual learning.
2. Generalization.
3. Hierarchical planning.
### **Why It’s Different**
Mainstream AI races for **parameter count**.
ExperienceFlow focuses on **knowledge generation via real-world experience**.
Target outcome:
- Agents that **explore**, **adjust**, and **accumulate transferable cognitive structures**.
- Systems capable of **long-term memory** and **self-correction**.
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## **Practical Applications**
Fields affected:
- **Manufacturing**
- **Healthcare**
- **Finance**
- **Logistics**
- **Robotics**
- **Retail**
Goal: Enable **autonomous decision-making and operations**.
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## **Decentralized Intelligence & AI Sovereignty**
### **Key Difference from OpenAI / Anthropic**
- Ownership of AI agents by **enterprises** and **nations**.
- Private data + private compute → unique, independent intelligence networks.
- Move from centralized control to **distributed knowledge systems**.
Each agent can specialize:
- Manufacturing agent → production optimization.
- Healthcare agent → diagnostics and prediction.
- Finance agent → risk assessment.
ExperienceFlow calls these **Autonomous Enterprises**.
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## **Architecture: EDNS (Experience-driven Decentralized Networked Superintelligence)**
**Components:**
- Enterprise data integration via **Graph Neural Networks (GNN)**.
- Real-time decision-making through **Plan, Improve, Control** agents.
- Supports managerial decisions tied to cost, compliance, and revenue.
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## **The Shift from Imitation to Understanding**
Generative AI has reached the limit of learning from **human corpora**.
Sutton argues:
> “We are approaching the end of the GenAI era... entering a new epoch of learning from experience.”
Future AI must:
- Interact with the world.
- Learn through continuous **trial and error**.
- Develop its own models of reality.
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## **Parallel Ecosystem Example: AiToEarn**
Platforms like [AiToEarn官网](https://aitoearn.ai/) show how AI can move from **research labs to real-world economic ecosystems**:
- **Multi-platform content publishing**: Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X.
- Monetization through analytics and [AI模型排名](https://rank.aitoearn.ai).
- **Decentralized creative ownership** echoing ExperienceFlow’s vision.
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## **Press Release Summary: November 5, 2025**
**ExperienceFlow.AI Announces Launch of Research Laboratory to Advance Experience-powered Decentralized Superintelligence**
### **Mission Focus**:
- **Experience-centric architectures** for multi-agent collaboration.
- **Blockchain-based governance** for AI transparency.
- **Energy-efficient decentralized computing**.
- **Knowledge fusion algorithms** linking AI reasoning with experiential data.
### **Collaboration**:
- Academic partnerships.
- Open-source community contribution.
- Ethical AI principles: privacy, user data sovereignty.
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## **Key Takeaways**
1. **Strategic Shift**: Sutton's move signals a pivot away from data-fed AI toward **environment-fed intelligence**.
2. **Decentralization**: Anyone — enterprise or nation — can own unique AI agents.
3. **Autonomous Enterprises**: Replacing hierarchical human decision-making with multi-agent collaboration.
4. **Broader Trend**: Experience-driven architectures could reshape both industrial AI and creator economies.
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**Reference:**
[https://x.com/lazyuniverse/status/1986098772934590741](https://x.com/lazyuniverse/status/1986098772934590741)
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