云启资本

云启资本

@yunqipartners

科技常新,寻找未来开创者

534 articles18 episodes

Articles

Yunqi AGI × WAIC2023 | Three New Opportunities for Large Model Deployment ## 01 In 2023, large language models have become the hottest topic in tech and investment circles. At this year's World Artificial Intelligence Conference (WAIC), discussions about large models were everywhere. From B2B to B2C, from infrastructure to applications, from text to multimodal — the entire ecosystem is being reshaped. But beneath the excitement, a critical question looms: where exactly are the real opportunities for large model deployment? At the WAIC Yunqi Capital AGI Forum, we invited entrepreneurs and investors at the forefront of large model development to share their perspectives. Through in-depth conversations, we identified three emerging opportunities that deserve attention. ## 02 Opportunity One: Vertical Industry Models The consensus among panelists was clear: general-purpose large models are important, but vertical industry models represent the more immediate commercial opportunity. Why? Because general models, while capable of many tasks, often lack the depth required for professional scenarios. In fields like healthcare, finance, and legal services, domain expertise — specialized knowledge, compliance requirements, workflow integration — creates significant barriers to entry. As one entrepreneur noted: "A general model might score 60 points on a

Yunqi AGI × WAIC2023 | Three New Opportunities for Large Model Deployment ## 01 In 2023, large language models have become the hottest topic in tech and investment circles. At this year's World Artificial Intelligence Conference (WAIC), discussions about large models were everywhere. From B2B to B2C, from infrastructure to applications, from text to multimodal — the entire ecosystem is being reshaped. But beneath the excitement, a critical question looms: where exactly are the real opportunities for large model deployment? At the WAIC Yunqi Capital AGI Forum, we invited entrepreneurs and investors at the forefront of large model development to share their perspectives. Through in-depth conversations, we identified three emerging opportunities that deserve attention. ## 02 Opportunity One: Vertical Industry Models The consensus among panelists was clear: general-purpose large models are important, but vertical industry models represent the more immediate commercial opportunity. Why? Because general models, while capable of many tasks, often lack the depth required for professional scenarios. In fields like healthcare, finance, and legal services, domain expertise — specialized knowledge, compliance requirements, workflow integration — creates significant barriers to entry. As one entrepreneur noted: "A general model might score 60 points on a

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Podcasts

Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built --- Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award. The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you. What surprised me wasn't that we won. It was that people kept using it *after* the demo. Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of. How? Three deliberate choices we made against hackathon orthodoxy. **First, we refused to build a chatbot.** The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right
Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built

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Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award.

The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you.

What surprised me wasn't that we won. It was that people kept using it *after* the demo.

Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of.

How? Three deliberate choices we made against hackathon orthodoxy.

**First, we refused to build a chatbot.**

The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right

Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built --- Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award. The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you. What surprised me wasn't that we won. It was that people kept using it *after* the demo. Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of. How? Three deliberate choices we made against hackathon orthodoxy. **First, we refused to build a chatbot.** The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right