作者：Guangmi，Penny，Cage，Haina，Feihong，Siqi，Nathan AI 领域的变化速率和格局演化永远比市场想象中更加迅速，几乎每个月市场共识和叙事都在翻 转。 本篇报告是拾象团队围绕这些变化做的一次系统复盘，用来重新校准对当下 AI 竞争时局的判断， 也对 2026 年可能成为主线的一些核心技术和产品趋势进行了拆解。 我们将这份报告开源出来，希望和大家共同探讨 ：哪些是结构性机会，哪些只是阶段性的噪音： 1. Google 重回叙事顶峰，但 AI 不是零和博弈， OpenAI 和 Anthropic 的"赢面"仍很大； 2. Continual learning 已经成为几乎所有 AI labs 押注的新范式共识，2026 年会看到新的信号； 3. AGI 竞赛很像自动驾驶，从 L3 到全面实现 L4 难度极大，但在知识类工作这些垂直领域，局部 L3/L4 已经实现了可观的效率提升和经济价值； 4. "NVIDIA + OpenAI" 这条主线在短期内可能被市场低估， 今天继续 bet OpenAI 是在下注 AI 时代 的 "something never seen"； 5. ...

作者：Penny 有悲观者认为，OpenAI 的护城河看不到了。模型没有壁垒，ChatGPT 没有网络效应，流量和算力 比不过 Google，高价值任务落后于 Anthropic。 客观说，这些因素是有道理的，2026 年刚过一个月，模型的格局不仅没有更稳定，反而更激烈 了。这也是 OpenAI 这个公司自发布 ChatGPT 以来，第一次打这种逆风局。 但我们对 OpenAI 还是抱有乐观信心，认为 2026 年能迎来叙事反转。以下是我们的 9 个关键判 断。 Insight 01 OpenAI 受到 Gemini 的影响有多大？ OpenAI 受到 Gemini 的影响主要来自三个方面：叙事、模型、流量。 从叙事上看，影响最大。Google 王者归来，让 OpenAI 跌落 SOTA 位置。也让大众意识到，OpenAI 从 4o 之后一直没发出大幅提升的模型， 不是 scaling 出了问题，而是 OpenAI 的 scaling 出了问题。 叙事直接体现在股价上，Google 自 Gemini 3 发布后涨了 20%，软银（OpenAI 在二级市场的映射）跌了 17%。 Google 王者归来，Anth ...

编辑：Penny 机器人领域是我们长期关注的赛道，而 Generalist 是当前机器人领域中极少数具备长期竞争潜力的 公司，核心优势集中在数据规模、团队能力与清晰的 scaling 路径上。 1. 高质量真机数据是机器人行业公认的核心稀缺资源，凭借 27 万小时的训练数据，Generalist 可能 是全球首个在数据规模上达到 GPT-1 量级的机器人团队，有领先其他团队 6-12 个月时间窗口。更 引人关注的是，去年 11 月，Generalist 宣称在机器人领域首次验证了类似语言模型的 scaling law。 2. 团队核心成员来自 OpenAI、Boston Dynamics、Google DeepMind 等机构，是 PaLM-E、RT-2 等 具身智能里程碑项目的主要贡献者，技术实力非常强大。 作者：Haozhen 3. 团队已经通过一系列的 demo 展示出了清晰的研究路径和模型出色的灵巧性。 我们认为，虽然目前机器人的数据依然非常匮乏，但如果模型性能可通过人类视频与真机数据的混 合持续提升，竞争焦点或将从数据规模转向数据配比。率先跑通并工程化最优数据配比的团队，可 能不仅能在性能上取得领先 ...

编译：Arlene、Haina Sequoia Capital 在 2026: This is AGI 这篇文章中断言 AGI 就是把事情搞定（Figure things out）的能 力。 如果说过去的 AI 是 Talkers 的时代，那么 2026 年则是 Doers 的元年。转变的核心载体正是 Long Horizon Agents（长程智能体）。这类 Agent 不再满足于对上下文的即时回复，而是具备了自主规 划、长时间运行以及目标导向的专家级特征。从 Coding 到 Excel 自动化，原本在特定垂直领域爆 发的 Agent 能力，正在向所有复杂任务流扩散。 作为 LangChain 的创始人，Harrison Chase 一直处于这场变革的最前沿。本文编译了 Sequoia Capital Sonya Huang & Pat Grady 访谈 Harrison Chase 的最新播客。作为站在 Agent 基础设施最前沿的先行 者，Harrison 揭示了为什么 Agent 正迎来其爆发的"第三个拐点"。 核心 Insight 提炼： • Long Horizon Agents 价值在于为复杂 ...

作者：Freda Duan （Partner@Alitmeter） 编译：Haozhen 近期 Claude Code 推出的 Excel 功能非常惊艳，我们认为 Excel 可能成为继 Coding 之后，下一个迎 来"aha moment"、并快速爆发的高价值领域。 本文是 Altiemeter 合伙人 Freda Duan 对 Coding 和 Excel 这两个 AI 垂直领域的深度解读，原文发布 于她的 Substack Robonomics。 简单来说，正如 Coding 凭借庞大的市场规模、向相邻场景自然延展的能力以及以产品驱动的 GTM 模式，迅速崛起为最强势的 AI 应用之一，Excel 也具备同样的条件： • 全球电子表格的 MAU 约为 15–16 亿； • Excel 可以延展至金融、运营、分析等场景，从某种角度看，大部分软件都可以被视为一层层叠加 在 Excel 之上的"Excel wrappers"； • Excel 可以通过用户自助完成快速采用（self-serve adoption）。 Coding 已经证明了这条路径下的爆发力，而 Excel 很可能是体量更大的下一站。 In ...

Core Insights - The article discusses the rapid rise of fal.ai as a generative media infrastructure company, providing a unified, low-latency API and cloud inference platform for high-performance access to multimodal generative models, including images, videos, and audio [2][4]. - fal.ai experienced explosive growth in 2025, with a revenue increase of 60 times over the past 12 months and a valuation tripling to $4.5 billion following a $140 million Series D funding round [2][5]. Group 1: Company Overview - fal.ai focuses on high-performance AI generative media platforms, enabling quick inference and deployment of various AI models through its API and cloud acceleration engine [4]. - The company completed a $140 million Series D funding round in December 2025, led by Sequoia Capital, with participation from other notable investors, raising its valuation to $4.5 billion [5]. Group 2: Market Positioning - fal.ai strategically chose to invest in generative video early on, recognizing the rapid growth in customer demand despite the market being perceived as niche at the time [6][8]. - The company believes that the market for generative video should be as large, if not larger, than that for large language models (LLMs), as video accounts for over 80% of internet bandwidth [8]. Group 3: Technical Advantages - fal.ai's team identified that video generation models face unique computational challenges, requiring significantly more processing power compared to LLMs and image generation [12][13]. - The company has developed a specialized tracing compiler to optimize performance across various video model architectures, allowing for efficient execution on heterogeneous hardware [15]. Group 4: Cost Management - fal.ai manages a distributed computing infrastructure across approximately 35 data centers, allowing for efficient resource allocation and cost management [17][18]. - The company strategically avoids traditional hyperscalers, opting instead to leverage emerging cloud providers (Neo-clouds) for more competitive pricing, which can be up to 2-3 times lower than hyperscalers [20][23]. Group 5: Ecosystem Development - fal.ai serves as a single hub connecting multiple model suppliers, allowing developers to utilize a wide range of models without being tied to a single provider [24][26]. - The platform supports over 600 generative media models, enabling developers to adapt quickly to the rapidly changing landscape of model performance and capabilities [24][26]. Group 6: User Engagement and Use Cases - Developers on fal.ai's platform typically use an average of 14 different models simultaneously, reflecting a modular approach to media production that allows for greater control and flexibility [32]. - The company highlights innovative use cases in education and gaming, such as personalized training videos and the potential for text-to-game applications, showcasing the versatility of generative media [35][37]. Group 7: Future Predictions - fal.ai predicts that within a year, fully AI-generated short films will emerge, with animation styles likely to see faster adoption than photorealistic styles due to lower production costs [41][42]. - The company emphasizes that the generative media industry will face a scenario where computational resources will be exhausted before data, indicating a unique growth trajectory compared to other sectors [41].

Core Insights - The article emphasizes that the Total Cost of Ownership (TCO) is highly dependent on the specific use case, suggesting that TPU is preferable for training and latency-insensitive inference, while GPU is better for prefill and latency-sensitive inference scenarios [3][4][5] - The fundamental difference between the 3D Torus and Switch Fabric (NVSwitch/Fat-tree) interconnect systems lies not in speed but in their assumptions about traffic patterns [4][5] - Google's historical TCO advantage established through TPU has been significantly weakened in the v8 generation [6] TCO Analysis - TPU v7 offers a cost advantage of 45-56% in training scenarios, based on the assumption that TPU's Model FLOPs Utilization (MFU) is 5-10 percentage points higher than that of GPUs [4][16] - In inference scenarios, GPUs (GB200/GB300) outperform TPU v7 by approximately 35-50% during the prefill phase due to their FP4 computational advantage [4][18] - The TCO comparison shows that TPU v8's cost efficiency has decreased, with the TCO ratio dropping from 1.52x for GB200/TPUv7 to 1.23x for VR200/TPUv8p [6] Interconnect Architecture - The 3D Torus architecture assumes predictable and orchestrated communication patterns, maintaining high MFU in large-scale training tasks, while Switch Fabric accommodates uncertain traffic patterns [5][38] - TPU Pods utilize a 3D Torus topology for high bandwidth and low latency communication, with a maximum cluster size limited by the number of OCS ports [31][34] Performance Bottlenecks - In training, the bottleneck typically arises from computational power and scale-out communication bandwidth, while in inference, the prefill phase is limited by computational power and the decode phase is constrained by memory bandwidth [12][22] - The performance requirements differ across training and inference scenarios, with TPU needing FP8 and scale-out bandwidth for training, while GPU requires FP4 and scale-up bandwidth for inference [12][13] Software Optimization - TPU's software optimizations aim to mitigate its inherent weaknesses in handling irregular traffic, transforming unpredictable workloads into stable data flows [46][47] - The introduction of SparseCore in TPU is designed to enhance its capability to handle dynamic all-to-all routing, acknowledging the need for communication-computation decoupling similar to NVSwitch [48] Competitive Landscape - Google TPU v8 adopts a dual-supplier strategy to reduce costs, collaborating with Broadcom and MediaTek for different SKUs, which impacts the overall design and production timeline [49][50] - Nvidia's Rubin architecture aggressively enhances performance and TCO for inference, with significant improvements in FP4 computational power and HBM bandwidth, positioning it as a strong competitor against TPU [51][52]

Core Insights - Agentic Commerce represents a significant shift in the way commerce operates, potentially transforming the landscape of internet advertising, e-commerce, and payment infrastructure if successfully implemented [2][5] - The article explores two main questions: 1) Can Agentic Commerce be commercially viable? 2) If successful, how will it reshape the distribution of benefits across the internet ecosystem? [5] Commercial Viability - The article reviews past failures of Meta and Google in e-commerce, contrasting their approaches with those of OpenAI and Perplexity, to identify which third-party models (3P) are most likely to succeed in the future [5][24] - The potential total addressable market (TAM) for three consumer behavior categories—Impulse Buys, Routine Essentials, and Life Purchases—is estimated to be $3 trillion, with Lifestyle and Functional Purchases being the most promising areas for Agentic Commerce [8][9] E-commerce Spectrum - E-commerce is described as a continuous spectrum, with Amazon and Shopify at opposite ends, defined by who acts as the Merchant of Record (MoR) [10][11] - The distinction between "Platform is the MoR" (e.g., Amazon) and "Merchant is the MoR" (e.g., Shopify) affects the business scale, merchant control over customer data, and the potential for disruption in payment systems [12][13] Agentic Commerce Paths - Perplexity and ChatGPT represent two different approaches to Agentic Commerce, with Perplexity acting as the MoR and ChatGPT allowing merchants to retain that role [14][19] - OpenAI's Agentic Commerce Protocol (ACP) decouples the front-end checkout experience from back-end payment processing, allowing merchants to maintain their existing payment service providers while integrating with ACP [15][18] Historical Context - Google and Meta's reluctance to become MoR contributed to their struggles in e-commerce, as they prioritized advertising revenue over the complexities of managing e-commerce transactions [24][26] - The article suggests that if Google or Meta had developed a protocol similar to ACP, their e-commerce trajectories might have been different [26] Impact on Advertising and Payment - The article discusses how Agentic Commerce could redefine the relationship between advertising costs and commission rates, likening both to a form of "digital tax" [32][33] - Shopify is positioned as a structural winner in the Agentic Commerce context, benefiting from its lack of MoR responsibilities and the potential for increased market penetration among small and medium-sized businesses (SMBs) [38][39] Future Considerations - The article envisions a future where a Universal Catalog could be developed to facilitate AI-driven shopping experiences, requiring rich and structured metadata to support precise consumer needs [44]

Core Insights - The AGI-Next 2026 event highlighted the significant role of Chinese teams in the AGI landscape, with expectations for further advancements by 2026 [1] - The article emphasizes the ongoing trend of model differentiation driven by various factors, including the distinct needs of To B and To C scenarios [1][3] - A consensus on autonomous learning as a new paradigm is emerging, with expectations that it will be a focal point for nearly all participants by 2026 [1][8] Differentiation - There are two angles of differentiation in the AI field: between To C and To B, and between "vertical integration" and "layering of models and applications" [3] - In To C scenarios, the bottleneck is often not the model's strength but the lack of context and environment [3][4] - In the To B market, users are willing to pay a premium for the "strongest models," leading to a clear differentiation between strong and weak models [4][5] New Paradigms - Scaling will continue, but there are two distinct paths: known paths that increase data and computing power, and unknown paths that seek new paradigms [8][9] - The goal of autonomous learning is to enable models to self-reflect and self-learn, gradually improving their effectiveness [10][11] - The biggest bottleneck for new paradigms is imagination, particularly in defining what tasks will demonstrate their success [12][13] Agent Development - Coding is essential for the development of agents, with models needing to meet high requirements to perform complex tasks [25][26] - The differentiation between To B and To C products is evident in agent development, where To C metrics may not correlate with model intelligence [27][28] - The future of agents may involve a "managed" approach, where users set general goals and agents operate independently to achieve them [30][31] Global AI Competition - There is optimism regarding China's potential to enter the global AI first tier within 3-5 years, driven by its ability to replicate successful models efficiently [36][37] - However, structural differences in computing power between China and the U.S. pose challenges, with the U.S. having a significant advantage in next-generation research investments [38][39] - Historical trends suggest that resource constraints may drive innovation in China, potentially leading to breakthroughs in model structures and chip designs [40]

Core Insights - The article presents 20 key predictions for AI trends in 2026, highlighting significant advancements and shifts in the industry [2] Group 1: AI Paradigms and Trends - The emergence of a new paradigm in AI, focusing on continual learning, is expected to gain traction in 2026, with positive signals likely to emerge from at least 1-2 technical pathways [5] - ChatGPT is projected to double its daily active users (DAU) to between 800 million and 1 billion by 2026, establishing itself as a global entry point for users [6] - The "App-store Moment" for ChatGPT is anticipated, leading to the creation of the first application generating $100 million ARR within its ecosystem [7] Group 2: Company Developments and Market Dynamics - OpenAI is expected to reverse its narrative in the second half of 2026, potentially achieving a valuation exceeding $1 trillion due to its strong market position and partnerships [9] - xAI's integration into Tesla is predicted to enhance the synergy between digital and physical worlds, contributing to advancements in AGI [11] - 2026 is forecasted to be a significant year for Enterprise AI, with Anthropic's ARR expected to at least double, reaching over $20 billion [12][14] Group 3: Technological Innovations - The multi-modal AI sector is anticipated to experience a commercial breakthrough, with the emergence of applications akin to Pokémon GO [15][16] - Long-horizon tasks and multi-modal demands are expected to drive the growth of new data companies, each achieving $1 billion ARR [17] - Personalization is projected to become a key competitive advantage for leading AI models, enhancing user engagement [19] Group 4: Market Valuations and IPOs - The AI IPO market is expected to flourish in 2026, with significant companies like SpaceX and OpenAI planning to go public, potentially signaling a peak in market sentiment [32] - Google is predicted to surpass a market valuation of $5 trillion, driven by its strong position in the AI model landscape and advertising business [34] Group 5: Infrastructure and Hardware - Nvidia's aggressive investment in optical interconnect technology is expected to lead to a wave of mergers and acquisitions in the CPO sector [27][28] - The demand for storage solutions is projected to surge due to the multi-modal revolution, integrating storage deeply into computational cores [29] - A significant increase in reasoning power is anticipated, with token consumption expected to grow by at least 10 times in 2026 [30][31]