124. Year-End Review [Standing Outside of 2025] A conversation with Dai Yusen about 2026 Expectations, The Year of R, Correction, How We Bet
Unconsciously, we have arrived at the last month of 2025. Amidst Beijing's first snow, we hope to reflect and look forward with everyone through a series: ["Beyond 2025"]. Today's guest is Dai Yusen, Managing Partner at ZhenFund. In episode 122, **Zhu Xiaohu claimed there would be no bubble within three years, calling the bubble theory pure nonsense, and urged entrepreneurs to advance at full speed in 2026.** **Yusen brings a fresh perspective today. In his view, the keyword for 2026 is "The Year of R"—Return and Research will once again become important. In a sense, 2026 will be a year of reality and recalibration.** **02:00 Review of 2025** 02:00 Progress from the model perspective: Thinking Time Scaling, represented by o1, has led to a significant improvement in model capabilities. OpenAI, Anthropic, and Google's flagship models are closely competing, each with unique characteristics, leading to rotating expectations and narratives. Chinese model companies have dominated the open-source ecosystem over the past year. 28:13 Progress from the application perspective: The first year of Agents means the problem won't be solved in a year; it's still an Early Market, needing to cross the chasm. Agent comes from Agency; the key is autonomy, saving human time, and being able to complete novel tasks and solve unseen problems. 52:31 How many projects did ZhenFund invest in in 2025? Around 20. Comparing the valuations of Chinese and US AI companies, Chinese companies hold high option value for the global market: Thinking Machine's angel round valuation, without a product, already equals the total valuation of Chinese AI companies. Model companies: Mistral 14b, Kimi 4b. Mistral itself doesn't focus much on pre-training anymore; benchmarks are essentially against Kimi. Application companies: In the US, a company like Manus, achieving 100m ARR in a few months, with dozens of percentage points gross margin and 20% MoM growth, should be valued at 3-5bn. **01:03:15 Forecast for 2026: The Year of R** **The Year of R: Return, Research, Remember, Multimodal Reasoning** **01:03:15 Return:** Why is Return important? ROI: The past 3 years have been about investment, as everyone was attracted by potential big returns. But now, as I (investment) gets larger, there's increasing focus on the realization of R (return), because R is what drives future I. Why do we believe there will be increased focus on return in 2026? Models: Model capability improvement is the most essential driving force of this wave of AI revolution, but model capability improvement is slowing down; leading US labs have much higher investments (Capex, labor, etc.), but this cannot prevent Chinese models from low-cost follow-up. Scaling Law cannot be simply understood as "huge investment leads to miracles." Applications: Application narratives have evolved from AGI to three main current lines; dreams are shrinking. Subscription model: It's difficult to raise prices for ordinary users; the low-hanging fruit has been mostly picked, and competition is intensifying. Advertising + E-commerce: Firstly, a large part is existing market share distribution; then, for new forms of applications, the speed is not as fast. Usage models like AI Coding/Image Gen: Usage will increase, but token prices will also decrease. Non-frontier intelligence will quickly become flat rate; only the most SOTA tasks can charge by usage. Previously valuable tasks will become less valuable; replacing many programmers doesn't mean earning those programmers' salaries. Enterprise services: This early market segment is limited in size, with many early adopters but retention may not be great. Microsoft Copilot continues to underperform expectations; large companies don't adopt new technologies that quickly. Conclusion: We need to achieve the accelerated GDP growth Satya mentioned, expanding the pie is true AGI, e.g., AI creating new drugs, discovering new knowledge, etc. Investment: US infrastructure construction is slow now, computing power depreciates fast, and labor costs are high, so returns need to be seen quickly. Market setup: At the end of last year, expectations were not high, but we saw rapid growth in ChatGPT, and certainty in Coding and Agentic brought application opportunities. Now, expectations are very high, investments are huge, but revolutionary new capabilities are not seen on the model side in the short term, and new paradigms are still brewing. Implications for entrepreneurs? The time for negative margin for growth is passing; quality growth is needed. The very loose financing environment (in the US) might slow down. **01:16:13 Research:** **new paradigm:** AI history has always seen step-function improvements; new paradigms are needed to bring about large increases in AI capabilities again. Ilya: scaling and research alternate; now it's time for research again. Currently, Online Learning, world models, etc., are important research directions. **neo labs:** Thinking machines, SSI, Reflection, to recent Humans&, Periodic, Isara, etc. Because engineering and product development are very different from research, a relaxed environment, a culture of free exploration, without time and KPI constraints, is needed. Everyone hopes neo labs can explore new paths that differentiate from current leading model companies. **new benchmark:** Current benchmarks can no longer effectively reflect the differences in AI capabilities, nor are they suitable as model training targets. How to measure a model that exceeds human performance in most areas? Yao Shunyu pointed out that the second half has arrived; new benchmarks are needed. Implications for entrepreneurs: Pay attention to the progress of frontier research; breakthroughs in research may unlock new application opportunities. **01:21:00 Remember (Memory):** Memory is a key differentiator for AI applications; current Memory capabilities have significantly improved ChatGPT's retention. Current Memory is still basically retrieval-based, not achieving true understanding. This area is also a fiercely contested battleground for research; if done well, it will bring further improvements. Proactive Agent: Memory and context are needed to unlock Proactive Agent opportunities, and Proactive Agents are very important because human active use of AI has limited intent; AI actively serving people can create 10x scenario opportunities. **01:24:06 Multimodality:** Visual Reasoning may have major breakthroughs; humans are essentially Pixel Machines, understanding the world through visual input. One can pay attention to the performance improvement of Zerobench, a Visual Reasoning Benchmark; current leading models are still basically below 10 points. Nano Nanana means image generation has entered a usable era like Sonnet 3.5. So what will be the Cursor of Image-gen? GPT-3.5 unlocked ChatGPT, Sonnet 3.5 unlocked Cursor, Sonnet 3.7 unlocked Manus. What application opportunities will Nano Nanana/Veo unlock? Using Imagegen/Videogen inside ChatGPT is clearly not a comfortable experience. Voice is a very important opportunity: better, more natural interaction, understanding user context. Plaud, Granola, Wispr flow/Typeless, Suno? **01:30:29 AI Bubble** From the secondary market perspective, there might be a major correction next year, possibly in the second half. The book "Boom: Bubbles and the End of Stagnation" mentions two types of bubbles: good bubbles and bad bubbles. If a correction is expected, what will be the changes in investment strategy next year? How will the secondary market transmit to the primary market? How to view Zhu Xiaohu's statement: "No bubble for at least three years," "Their arguments are pure nonsense"? "Personally, I am completely out of the market right now." The valuation gap between China and the US is expected to narrow. **01:47:38 Entrepreneurship Changes and Advice** Based on the "Year of R" theory, what advice for entrepreneurs? How to judge founders in the AI era? What's the biggest difference from the internet era? Entrepreneurship is like F1 racing. Any projects missed in the past two years? Which directions have seen incremental growth due to AI? What are good interactions besides chatbots? This year, I personally talked to 150 projects, only invested in 2. **02:18:31 Also Talk About Life** Reflections on personal life: This year's reading, thinking, and life. Reflections on VC: Young investors need to differentiate themselves. Reflections on ordinary people: Learn to live in intelligence abundance. **02:29:50 Final Q&A** **Last question: You proposed the Year of R, and you've cleared your secondary market stocks. So, will you short them?** **02:36:10 At the end of this episode, I've included a casual chat with Yusen before the recording. We commented on some frequently discussed AI companies. If you find it interesting, you can continue listening.** **02:36:30** OpenAI 02:46:38 Google (I don't think Gemini can stop ChatGPT's growth, nor do I think Google is out of danger.) 03:06:36 Anthropic 03:11:05 Manus 03:19:47 Thinking Machines Lab, Safe Superintelligence Inc. Year-end Review ["Beyond 2025"]: 《122. Zhu Xiaohu's Third Installment of Realist Stories: The AI Feast and Bubble》 [More Information] Disclaimer: This content does not constitute investment advice.
Original title: 124. 年终对话【站在2025年之外】和戴雨森聊2026年预期、The Year of R、回调、我们如何下注
Original description: <figure><img src="https://image.xyzcdn.net/Flo18nNUSP7OUNlTf8UgCdHxio6O.jpg" /></figure><p>不知不觉,我们来…