--- title: "Unique Viewpoint | DataCanvas Fang Lei: AI Compute Infrastructure Is the More Urgent Layout Now" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2024-03-27T17:15:00+08:00" canonical: "https://ffcap.cn/en/research/20240327a07hlc00" source: "https://ffcap.cn/en/research/20240327a07hlc00" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20240327A07HLC00" --- # Unique Viewpoint | DataCanvas Fang Lei: AI Compute Infrastructure Is the More Urgent Layout Now _Unique Research · Unique Viewpoint · March 27, 2024_ Background: On **January 24, 2024**, Unique Capital and partners hosted the **2024 AIGC Application Development Summit** in Shanghai, with nearly **1,000** AI practitioners. This piece is compiled from the guest dialogue. ## Guest - **Speaker:** Lei Fang (方磊) — Chairman, DataCanvas / Jiuzhang Yunji (九章云极) - **Host:** Xiaojing Hu (胡小婧) — Partner, Unique Capital ## Summit dialogue ## Q: ChatGPT launched a distinctive tech wave. As a founder who has worked AI for a decade, how does it feel? **Lei Fang:** We would not claim to have spanned two full AI waves and cycles — only that our team started a bit earlier and has stayed longer. Years ago another co-founder and I returned from Microsoft to found China’s first machine-learning platform company. Today our strategic positioning is **AI Foundation Software** — a full stack of model services for customers. DataCanvas’s path has several stages: **First stage (2014–2015).** We did not give models to end users; we gave developers tools to build models. Public attention then was more on O2O. **Second stage (2016).** AlphaGo beating Lee Sedol lit up AI. Most still believed that in China, AI value would come from end-to-end services — tools alone could not create value for people. **Third stage (2020–2023).** After 2020, as first-wave AI landed and category leaders formed, people found the last mile was not simple. Precisely because that mile is hard, tool value became visible. We grew fast; leading enterprises in benchmark industries are largely our customers. **Fourth stage (2023–now).** 2023 was a huge turn — no need to belabor it. Any technical person is startled the first time they use ChatGPT; especially people like us — algorithm-trained, paper-publishing, PhD-track, then founding in AI. Traditional methodology made us slow to believe LLMs; people deepest in ML/DL were often the most skeptical at first. My first ChatGPT use was also a shock — an upgrade of prior knowledge. Before LLMs, our customers’ largest GPU clusters were about **300** cards — we already thought that was too many to use well, so there was much GPU virtualization to train many small models in parallel (PyTorch). Now **300** is too few: even a small **7B** model needs **500+** cards. That hits people with traditional backgrounds hard — and is an unprecedented opportunity for the whole AI industry. Most people hope to put AI into personal life and work. For me, generative AIGC’s rise drives huge compute consumption — meaning China’s digital-economy new infrastructure, the **compute economy**, is booming. AI apps will keep emerging into daily life. Yet next-era **AI-native applications** will not appear overnight, however much people want them. Patience matters. Personally I do not expect a killer app this year. Douyin arrived in **2013**; the iPhone entered mass view in **2007** — longer than anyone imagined. Patience is essential. Given China’s characteristics: before a killer app appears, the country can use the window to lay infrastructure. We are in an era that needs expanded compute infrastructure. So I was very excited about 2023: not only large models advancing the industry, but a year for China to lay out AI infrastructure. ## Q: How should the industry see ChatGPT’s revolutionary role? **Lei Fang:** People long in NLP or Transformer pretraining may be less shocked; still, most first-time ChatGPT users marvel at its reinforcement learning and human interaction. Two years ago, after _Attention Is All You Need_, many built Transformer vision models — but still doubted final effect. Today the shock is at the **product** level. OpenAI’s tech is not unique — the base came from Google papers — but product and tech have a large gap. ChatGPT looks simple yet is, in a sense, humanity’s first AI-native product. OpenAI’s power is that the product flywheel has started. I do not think that maps purely to tech; a small tech step that guesses the right direction can be a huge product step. We still have many tech options. Many say OpenAI is far ahead; I disagree. History of first products has three cases: Yahoo made search, Google made it revolutionary and monopolistic; iPhone is excellent and owns half the phone market, yet Android is a strong rival; Netscape made the first great browser and was then killed by Microsoft. So OpenAI need not be the absolute lasting leader. I still hold that view. Over the past year we saw more tech possibility and change. Is Transformer best? Are Nvidia GPUs always most efficient? Not necessarily. Humans may find a more elegant, simpler, more symmetric Transformer — then much of the stack changes; or build specialized chips on OpenAI Triton for more efficient compute — all lower-layer change — plus AI-native interaction modes and product forms. ChatGPT’s biggest gift is showing AGI is **reachable**. From chips to new Transformer architectures and beyond, people are exploring. In the next two to three years we may see more base-tech change and new AI-native product forms — we will meet them soon. ## Q: China is concentrating on infrastructure; compute is scarce; a compute economy has emerged; buying cards is hard. Your view? **Lei Fang:** Two sides. Stats: in **2023**, global H100 demand was about **1.7M** units; Nvidia produced about **1.5M**. For **2024**, H100 demand for training + inference (inference rising faster) is about **4.6M**; Nvidia’s expected output will not exceed **3M**. Globally, compute is relatively scarce. Geopolitics adds friction: China cannot buy Nvidia cards smoothly under sanctions. Domestic GPU vendors exist, but catch-up takes time. So compute remains relatively scarce. China is building intelligent compute centers at scale; some fear overcapacity. I do not. On current plans we estimate China will have **500–1,000** intelligent compute centers in three years — a striking scale. Today “compute serves AIGC apps” is an indirect logic; the direct logic is infrastructure investment — which needs foresight and courage. Infrastructure is moderately ahead of demand — more investment logic than pure usage/application pull. China’s current compute build is investment and infrastructure logic, not AIGC-application consumption logic. That itself becomes digital-economy new infrastructure — the compute economy. Local governments, SOEs, and industry leaders fund many centers: national infrastructure and a possible overtaking chance. In the public-cloud era, China’s public cloud lagged the U.S./global commercial high-value cloud. In the LLM era, building **500–1,000** intelligent compute centers nationwide can be read as building a decentralized GPU cloud (versus overseas players somewhat like CoreWeave). So even if we did not build high-commercial-value public cloud in the CPU era, in the GPU era intelligent compute centers may go far. Under today’s compute economy, nationwide intelligent-compute rollout is not only an investment opportunity with deep industry impact — it may be China’s best chance to build a **GPU public cloud**. ## Q: In ToB, how do you see the AI landing path? **Lei Fang:** Landing views differ. Generative AI can do many things, but what people accept most today is the “knowledge agent” module — typically LLM-based **RAG**. Previously unstructured knowledge had only search; with LLMs, RAG can output better. Example: a top Shanghai steel manufacturer — new factory workers lack equipment-maintenance knowledge; train/fine-tune an LLM on traditional work orders to answer efficiently. In plain terms: teach search to speak human. More plain language is a big step for many ToB firms. Past enterprise architecture handled structured data and warehouses reasonably; unstructured data capability was near zero — mostly cloud-disk storage. Now we first raise unstructured processing. Structurally: structured data via warehouses, data lakes, and stores; unstructured is catching up fast. As RAG improves, unstructured will match structured handling. Eventually all data — structured or not — may be handled as vectors. That is a huge enterprise-architecture shift affecting business models, industry structure, sales models, and more. Those structural changes let enterprises treat unstructured data like warehouses — process, analyze, cognize. That is key to landing, and where they start first. Versus OpenAI or top model vendors, most enterprises’ real moat is **private data**. Without that premise the enterprise AI market would not exist. Samsung needs process secrecy in semiconductors; many firms forbid OpenAI/open models and push self-trained models — essentially a data-boundary problem. Data boundaries will keep defining much else. Back to enterprise AI: the fundamental defining boundary is private data. _3 CN images (summit/guest); attach on ingest. Full Q&A preserved._ — END — --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240327a07hlc00 On-site reading page: https://ffcap.cn/en/research/20240327a07hlc00