--- title: "Unique Capital’s Hu Xiaojing: Is Generative AI Overestimated or Underestimated?" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2023-10-18T18:00:00+08:00" canonical: "https://ffcap.cn/en/research/20231018a081xx00" source: "https://ffcap.cn/en/research/20231018a081xx00" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20231018A081XX00" --- # Unique Capital’s Hu Xiaojing: Is Generative AI Overestimated or Underestimated? _Unique Research · Insight · October 18, 2023 · Chaos Innovation Institute AI series live #6 · Partner Hu Xiaojing (Unique Capital)_ “In H1 2023, China’s generative-AI financing totaled **RMB 7.399 billion**. Silicon Valley’s genAI primary market saw **42** rounds totaling **$14 billion** — China’s figure is about **7%** of that.” “China’s genAI application stack shows distinctive traits.” “Take ChatPPT — one-click PPT generation. Paid users already exceed **10,000** with million-RMB product revenue — rare in today’s AI market.” — Hu Xiaojing, Partner, Unique Capital. Is every industry about to be remade by AI, or is the market early with more foam than substance? Overestimated or underestimated? Subjective opinions abound; what’s needed is data. ## H1 2023: China vs U.S. genAI financing China’s H1 genAI total: **RMB 7.399B**. June alone showed **RMB 5.23B** in disclosed deals — a steep chart peak that mostly reflects _disclosure timing_, not a June explosion. Unique Capital estimates ~**30%** of deals (including some LLM and strategic rounds) still undisclosed, so 7.399B is conservative. Full-year 2023 China genAI financing could exceed **RMB 15B** on an optimistic Unique Capital view. 2021 was a prior China AI heat (the “Four Little Dragons” listed). 2023’s genAI wave is accelerating within AI. Among Top-10 AI financings, **MiniMax (Mingzhi Dream)** stood out as the highest-funded non-giant LLM startup (with Tencent Investment). Active strategic/corporate players: Tencent Investment, Kunlun Tech, Meituan, Ant — a larger corporate-VC share than classic independent VC. U.S. Silicon Valley H1 genAI: 42 rounds, **$14B** (~RMB 100.6B). China’s 7.399B ≈ 7%. Read-through: - China’s foundation layer (chips, infra) is earlier; the U.S. foundation layer is mature. - China raises across foundation + model + application at once; the U.S. (even aside from OpenAI) spends less on raw foundation build and more on foundation LLMs, vertical apps, and middle-layer tools (vector DBs, AI toolchains, deployment). - Abroad, relatively mature genAI business models already exist; China is still largely in validation. Near-term job: get infra to a solid “60-point” baseline, then raise precision — then form China-specific application patterns. ## Five traits of China’s genAI application ecology 1. **Colder capital, higher bar.** RMB 7.399B looks muted vs U.S. heat. Domestic LPs demand high usability and “10× better”; Silicon Valley often funds R&D/PoC stages more readily. 2. **Labor-cost structure.** Average China labor is cheaper, but some skilled domains (e.g. legal) are expensive — B2B efficiency pressure is intense; software must clear a higher “10×” bar (Silicon Valley often accepts ~5×). 3. **Weaker open community culture** vs Silicon Valley open AI / open source. This wave favors experienced founders (mobile-internet, NLP, AI-native) and industry incumbents with customer scenes and data. 4. **Richer scene and commercial data** from China’s internet vertical penetration — ability to reach “60-point” LLMs fast, then fine-fill for moats. 5. **Industrial structure.** Internet/high-tech are not absolute GDP dominants; smart manufacturing still leads. Align with 14th Five-Year Plan and generative-AI regulation: AI’s China mission is empowering the real economy — different from Silicon Valley’s new-tech-first lens. Metaphor: raising a child poor vs rich — China is in the “raise lean” phase: identify problems, ship solutions, self-fund quickly, and attach to industry. China’s AI industry scale reached ~**RMB 195.8B** by 2022 (purple zones on Unique Capital’s map = validated scale apps: internet marketing, risk/security, scheduling, robotics, dialogue). CAST chair Wan Gang’s line — empower the real economy, support social development — matches that judgment. ## Case studies: Chinese genAI products that already work **ChatPPT (consumer):** one-click decks + deep document layout; >10k paid users; million-RMB revenue; ~20 employees. **Novel Monkey (consumer):** web-novel model trained on massive fiction; one-click ~100k-character novels; ~**$100k/month** revenue; if views hit a threshold, humans edit plot into bestsellers; only **3** employees — tech leveling for tiny teams. **Kuaizi Tech (B2B video):** splits commercial video into workflow modules (edit/shoot/cut/post/manage). Example: MINISO store managers need frequent geo-local Douyin/Xiaohongshu posts — AI mashups, voiceover, effects, and recommendation rules cut cost and lift acquisition. Post-ChatGPT awareness, customer and revenue growth accelerated (>50% MoM cited). **Lanma Tech (B2B AI Agents):** agents as digital employees — autonomous decisions and resource orchestration vs traditional SaaS. Sales leads can ask in natural language for charts from internal sales/SKU data. Lanma is already working with large enterprises. Takeaway for industry buyers: prepare data storage and knowledge bases so AI products can call them later. Market skew: more B2B than consumer cases in financing — partly fear that stronger foundation models erode consumer moats; partly limited LP dry powder preferring faster-payback B2B over capital-heavy consumer growth. ## Advice for industry + AI companies — and the over/under question Hu: over- vs under-estimated is less important than case-by-case analysis. Two factors to judge whether a tech wave is over/under-hyped: 1. **Tech + scene stability.** Mobile internet’s stable phone scene spawned App Store empires (Meituan local life in months; Pinduoduo on WeChat to IPO in ~3 years). We’re in AI’s “decade.” Shift from GUI to language/dialogue interface (LA) is real — but dialogue-native apps have not yet mass-adopted; users still mostly search. Application ecosystems typically need 3–8 years once tech is stable and scenes are widespread. 2. **Clear business model + value network.** Foundation/open models + application layer with clear supply–demand links work. Fuzzy zones: simple dialogue tasks many assume foundation models will eventually swallow. Baidu’s Wenxin Fund (RMB 1B) to back Wenxin-based builders is ecosystem strategy — foundation models must define what they _won’t_ do, stay restrained, and light others’ value networks (Chaos’s philosophy). --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231018a081xx00 On-site reading page: https://ffcap.cn/en/research/20231018a081xx00