How to Find Your New Position in the AI Wave Over the Next Decade
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## Editor’s Note
In today's era — where **artificial intelligence** and **globalization** converge — innovation is reshaping the entrepreneurial landscape with unprecedented **speed** and **depth**.
On **October 12, 2025**, **NUS Business School EMBA**, along with Silicon Valley’s **Plug and Play Innovation Center**, hosted the **“NUS New Global Entrepreneurs Silicon Valley Forum (Season 3)”**.
This global gathering brought together visionaries and action-takers to discuss:
- How to **“move fast yet go far”** in the AI era
- How Chinese and global founders can find **unique positions** across diverse cultures and markets
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## 1. The Speed & Depth of AI Entrepreneurship
### How to Balance Fast Launch and Deep Expansion
**Speaker:** **Arvin Sun**, Founder & CEO of **Traini** — the world's first **pet empathy AI dialogue platform**.
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### AI-Native Thinking: Making Abstract Problems Computable
> Real needs existed before AI — AI’s value is in **translating fuzzy, human problems** into scalable, computational problems.
> Drill **100 meters deep** in a **1-meter-wide** niche.
**Keywords:** AI-native, pain points, vertical deep dive, computable transformation
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### Key Concepts
#### 1. AI-Native Company vs. AI-Native Application
- **AI-Native Company**:
Organization and R&D adapted for the AI era; even non-engineers directly contribute to the core product via tools like *Vibe Coding*.
- **AI-Native Application**:
Product built from the ground up with AI architectures (*e.g. Transformers*) — AI is the **core value**, not an add-on.
**External Signal:**
Watch adoption rates among **pre-university youth** — strong early user penetration signals long-term viability.
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#### 2. Real Pain Points Pre-Existing AI
Needs don’t emerge because of AI — AI simply offers a **better solution**.
- Arvin’s pivot: from “pet food delivery” to “pet behavior understanding”
- Households spend $3k–$6k annually on dog training
- The **real pain point**: systematic understanding and improvement of pet behavior
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#### 3. Abstract → Computable
Pre-large models: traditional ML + expert connections had limited emotional/behavioral understanding.
**Stages of AI Adoption:**
1. **Enhancement Stage** — Overlay large model, explore capabilities
2. **Demystification Stage** — Recognize AI equals *compute power × data × right algorithms*
3. **Native Stage** — Build own **vertical small models** for pet emotion/behavior understanding
**Takeaway:** AI excels at turning **abstract, fuzzy** human problems into scalable product capabilities.
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#### 4. Closing the Gap: What Users Want vs. What Product Can Do
To scale in AI-native ventures:
- Stay **close to the true problem**
- Expand **precisely and vertically**
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💡 **Pro Tip:** AI-native content publishing platforms like [AiToEarn官网](https://aitoearn.ai/) help creators:
- Publish across global channels
- Analyze cross-platform performance
- Monetize creativity efficiently
Open-source connections extend to Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter).
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### 5. Source of Confidence: Scenario & User Validation
**Industry Validation:**
- Smartphone & EV brands approach proactively for collaboration
**User Validation:**
- Intense emotional feedback during critical life moments with pets drives long-term user loyalty
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### 6. Methodology: Interest-Driven Persistence
Two entrepreneurial paths:
- **Trend-driven** — Enter as market rises
- **Passion-driven** — Start from expertise & love, dig deep over time
**Arvin’s Process:**
1. Identify a **real need**
2. Use AI to **compute** the abstract
3. Focus narrowly — drill deep
4. Refine product with **young user validation + industry distribution**
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## 2. AI-Native Education: **BeFreed**
**Speaker:** **Jisong Liu**, Founder & CEO — AI Education Innovator
### Lightweight Learning
> Scarce in learning is not knowledge, but **attention**. AI amplifies human bandwidth.
**Keywords:** lightweight content, AI-native education, focus validation, signal-to-noise ratio
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### Challenges
- Online courses: <5% completion
- Books stall after Chapter 2
- Saved social content largely unopened
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### Key Insights
1. **Demand validated** by high engagement in short-form formats (*TikTok, Pinterest*)
2. **Supply boosted** by AI lowering creation costs
3. **Product Validation** — 1-week MVP → 100k+ users, 5k+ paying
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**Focus Principles:**
1. **Trade-offs control noise**
- iOS-only, English-only during early stage
2. **Goal orientation**
- 1,000 passionate fans > 100,000 casual users
3. **Boundaries matter**
- Use signal-to-noise ratio to prevent premature scaling
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💡 Platforms like [AiToEarn官网](https://aitoearn.ai/) embody these principles — focus first, scale later — while enabling **global publishing & monetization**.
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## 3. AI in Art Accessibility: **ARTLAS**
**Speaker:** **Grace Yao**, Founder & CEO — AI Art Exploration
> AI removes boundaries in curation, translation & storytelling — making **art reachable for everyone**.
**Keywords:** AI Art, Cultural Accessibility, Cross-Industry Trust
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### Process:
1. Identify **latent demand**
2. Boost supply efficiency via AI-generated multilingual curation
3. Validate via strong online/offline engagement
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**Breakthrough:**
Invite influential museum advisor → open collaboration channels
💡 **Use empathy to align solutions with partner pain points**.
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[**AiToEarn官网**](https://aitoearn.ai/) parallels this — bridging content creation with **cross-platform reach** and **cultural impact**.
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## 4. AI-Native = Interest × Mission
Core Philosophy:
Interest sparks creation → Mission sustains it through product evolution.
**Application:**
From personal memory preservation → “digital biography” products via AI prompts
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