This is the 8th episode of "Global Large Model Quarterly Report," and it's also the third year of my New Year's Eve conversation with Guangmi!This episode will show you, at a time when AGI is increasingly filled with a return to realistic sentiment, what teams, factions, and alliances have formed in the global AI War? What new paradigms are various frontier labs exploring? And what new types of research labs have emerged in Silicon Valley?If you have more thoughts or suggestions for the Global Large Model Quarterly Report, please feel free to leave a comment, and we will see them all.By the end of 2025, we look forward to making progress together with AI!AI War: A Competition Global Giants Cannot Afford to Lose02:00 The Global Large Model Quarterly Report has accompanied everyone to its 8th episode03:19 Let's start by talking about the AI Bubble, as is customary07:38 OpenAI's Revenue Composition Analysis: Visible and Invisible Income13:10 Some companies have "pawn value for giants"13:32 The issue of OpenAI's commercialization speed15:04 Looking at the big picture, the main drivers and factions in this AI War: Nvidia GPU vs. Google TPU17:16 The stronger Google gets, the more anti-Google alliances will form; the stronger OpenAI gets, the more anti-OpenAI alliances will formAlternating Leadership is the New Normal for Top Models17:48 The world's three leading models GPT/Claude/Gemini, alternating leadership is the norm in competition25:40 Here's a lazy judgment: foundational models = comprehensive e-commerce, scale SKU = scale data27:40 With Gemini's rise, what will OpenAI do, people wonder? How to view the competition between these two?31:20 Another judgment is: ultimately, ChatGPT will integrate with traditional Search and eventually capture a share of traditional Search advertising.35:08 People no longer consider Google an AI loser like Nokia, but Google's crisis has not truly been resolved.The Third Paradigm After Pre-training and RL: Online Learning36:01 Pre-training scaling is indeed nearing its end, but Online learning is just beginning38:49 OpenAI remains very strong even after splitting 3-4 times: Anthropic was OpenAI's earliest Scaling team, Ilya was the Pre-training team, Thinking Machines was the original ChatGPT and Post-training team40:01 Here's a bold claim: many of the robotics, world models, and multimodal issues people raise might be false problems; Online learning might be the only truly important problem.41:01 Pre-training is oil, fossil fuel; RL expert data is new energy, useful but limited in total; Online Learning is nuclear fusion, not yet broken through, but if it breaks through, it will be invincible, and humanity will enter the silicon-based era.Is AGI like a Marathon or Autonomous Driving? A War of Attrition + Cash Flow Battle43:05 If a model's data distribution doesn't contain such data, these tasks won't work; only by compressing such data will they work – today's models are still huge compressors.44:33 "Model is Product, Data is Model"44:45 Heard a rumor: Sam said internally to forget about AGI for now?45:04 Local L3/L4, difficult for overall L4: More realistically, among knowledge workers, the experience of local L3/L4 is observable, such as ChatGPT for long-tail information retrieval, Coding Agent, Office/PPT/Excel Agent, Finance Investment Research Agent.Current Thoughts on Investment (Not Investment Advice)47:11 Last podcast mentioned 40% OpenAI + 40% Bytedance + 10% Google + 10% Anthropic. Now it's: 25% OpenAI + 25% Bytedance + 10% Google + 10% Anthropic + 10% Nvidia + 10% TSMC, a bit for each. Also, today we should bet on the paradigm and winner three years from now; Neo Labs like Thinking Machines and SSI should also be seriously considered.2026: Important Trends and Signals in the Bay Area50:57 Investment themes to look forward to in 202652:53 Model is Product, Data is Model54:48 Horizontal and Vertical: Horizontally distill human expert knowledge, horizontally expand into more industry domains; Vertically means the next generation technology paradigm, Online learning, creating higher economic value.56:45 Distribution map of newly emerging Neo Labs in Silicon Valley59:43 Latest developments and company distribution in Robotics01:05:55 ARR growth status of top Silicon Valley companies: The more prominent the company, the cheaper it is; the more prominent the company, the less of a bubble it has.01:08:02 Domestic large model and application companies01:09:39 What's the next decisive move for models?Chinese Entrepreneurs, Funds, and "China's Silicon Valley"01:10:16 Differences in AI narratives between China and the US01:12:15 What to say to Chinese entrepreneurs01:14:20 Why do we say we hope to promote a Silicon Valley in China?01:16:45 Will the world's leading AI companies in 3-5 years be Chinese teams?Year-End Dialogue [Standing Beyond 2025]《122. Zhu Xiaohu's Third Installment of Realist Stories: The Feast and Bubble of Artificial Intelligence》《124. Chatting with Dai Yusen about 2026 Expectations, The Year of R, Pullbacks, and How We Bet》《125. Chatting with Altimeter Partner Freda: Betting on OpenAI, Robinhood's Past, America's Capital Bad Boy, Abacus and Bubbles》《126. Chatting with Sequoia's Zheng Qingsheng: The Traffic Revolution in Economic History, the Unpredictability of Human Behavior Patterns, and Founder Personalities》【More Information】Disclaimer: This content is not intended as investment advice.
Original title:
127. 大模型季报跨年对谈:和广密预言一场AI War、两大联盟和第三个范式Online Learning
Original description:
<figure><img src="https://image.xyzcdn.net/Flo18nNUSP7OUNlTf8UgCdHxio6O.jpg" /></figure><p>这里是《全球大模…