<p>“我认为这可能是一个罕见的案例，双方公司都能获利。两家公司都可以在人工智能领域拥有庞大的广告业务，与商业相辅相成。” 这是 Deepwater Asset Management 的管理合伙人 Gene Munster 的观点，这一观点与当前主导人工智能投资领域的零和博弈叙事相悖，特别是在 Alphabet（谷歌）与 OpenAI 之间的竞争中。传统观点往往将成熟的搜索巨头与颠覆性的初创公司对立起来，但 Munster 认为，革命正在显著扩大可寻址市场的总量，因此共存和共同增长是高度可能的结果。</p>
<p>Munster 在 CNBC 的《收盘铃》节目中，对当前人工智能交易的状态进行了深刻分析，不仅强调了在广告等面向消费者的应用中的竞争动态，还指出了投资者对推动人工智能经济所需后端基础设施的基本误解。他的评论为专注于真正长期价值创造的创始人和风险投资者提供了重要的警示。</p>
<p>谷歌和 OpenAI 都能在广告领域蓬勃发展的想法源于对消费者行为变化的观察。随着大型语言模型逐渐融入日常工作流程，消费者越来越多地通过对话界面寻求信息——无论是通过增强了人工智能的传统搜索框，还是通过专用的聊天机器人。Munster 认为，这种信息寻求的扩展意味着搜索信息所花费的总时间在增加，为货币化提供了新的空间。采访者使用大型语言模型为她的服装选择珠宝的事实突显了这一应用转变的紧迫性和广度，表明人工智能正在超越纯技术任务，进入消费者商业和生活方式策划领域——这些都是适合上下文广告的领域。</p>
<p>这一广泛的应用层依赖于仍处于初期阶段的基础设施建设。根据 Munster 的说法，人工智能交易已经成功通过了 2024 年的前两个检查点——来自 TSMC 的积极指导，受加速的人工智能需求驱动，以及 Nvidia 在 CES 上的鼓舞人心的评论。下一个关键障碍，预计在 1 月底和 2 月初，将是超大规模云服务商（谷歌、微软、亚马逊）的资本支出（CapEx）指导。超大规模云服务商的资本支出增长预期已经从 30% 上调至 34%，Munster 预计可能会达到 40% 或更高，表明对必要硬件基础的深度承诺。</p>
<p>然而，投资者对这一基础设施建设的更深层次、结构性误解依然存在。当前的叙事往往集中在训练上——构建和完善大型语言模型本身的昂贵且计算密集的过程。Munster 认为，这种关注错失了整体，指出投资者 “陷入了人工智能是关于训练的思维模式。”</p>
<p>长期的经济现实，受到每日使用量的驱动，明显指向推理。推理是运行训练模型以生成结果的过程——人工智能的实际 “思考” 和智能应用。Munster 引用 Deepwater 的内部研究和行业数据来强调巨大的规模差异：“去年 2025 年的推理市场是训练市场的五倍。” 展望未来，这种差距将呈指数增长。</p>
<p>人工智能的智能不在于训练，而在于应用。随着数百万用户、企业和专业代理开始依赖这些模型进行日常任务，专用于推理的计算周期将远远超过专用于训练的周期。Munster 强调了未来需求的规模，表示 “推理将比训练大数十万倍；智能在于思考，而不是训练。” 这种差距表明，专注于优化推理——人工智能的实时部署和扩展的公司，远比那些仅专注于模型创建的公司更具爆发性增长潜力。</p>
<p>这一基础设施动态对市场结构有直接影响。虽然 “魔法七”（最大的科技公司）主导了初期的人工智能热潮，但所需基础设施投资的庞大规模，加上对专用推理芯片和服务的广泛需求，表明人工智能机会将向下渗透。Munster 坚持他的预测，专注于基础设施和应用层的小型公司将开始超越大型公司，推动强劲的盈利修正，随着推理市场的加速。最近在 AMD 等公司的市场强劲表现，受到积极的供应链消息的推动，是这一基础设施供应链广泛参与的早期指标。</p>
<p>对于创始人和风险投资者来说，结论很明确：人工智能的淘金热正在从挖掘黄金（训练）转向销售铲子和处理工具（推理）。下一阶段的巨大价值创造将出现在那些能够在技术和商业的各个方面实现经济、可扩展和普遍部署人工智能智能的公司，确认人工智能基础设施建设远未完成。</p>

OpenAI

谷歌-C

谷歌-A

<p>Deepwater Asset Management 的 Gene Munster 认为，随着消费者行为的变化，Alphabet 和 OpenAI 都可以在人工智能广告领域蓬勃发展。他强调了人工智能基础设施的重要性，并指出虽然投资者关注训练，但真正的增长在于推理——即人工智能模型的应用。Munster 预测，优化推理的公司将会看到显著的增长，建议将投资重点从训练转向基础设施和应用层，因为人工智能市场将继续演变</p>

<p>“I think that this can be one of those rare cases when both companies can win. Both companies can have massive advertising businesses within AI, adjacent to commerce.” This assertion by Gene Munster, Managing Partner at Deepwater Asset Management, cuts against the prevailing zero-sum narrative dominating the AI investment landscape, particularly concerning the rivalry between Alphabet (Google) and OpenAI. The conventional wisdom often pits the established search behemoth against the disruptive startup, but Munster argues that the revolution is expanding the total addressable market so significantly that coexistence and mutual growth are highly probable outcomes.</p>
<div class="lb-trans"><p>“我认为这可能是一个罕见的案例，双方公司都能获利。两家公司都可以在人工智能领域拥有庞大的广告业务，与商业相辅相成。” 这是 Deepwater Asset Management 的管理合伙人 Gene Munster 的观点，这一观点与当前主导人工智能投资领域的零和博弈叙事相悖，特别是在 Alphabet（谷歌）与 OpenAI 之间的竞争中。传统观点往往将成熟的搜索巨头与颠覆性的初创公司对立起来，但 Munster 认为，革命正在显著扩大可寻址市场的总量，因此共存和共同增长是高度可能的结果。</p>
</div><p>Munster, speaking on CNBC’s Closing Bell, provided a sharp analysis of the current state of the AI trade, highlighting not only the competitive dynamics in consumer-facing applications like advertising but also the fundamental misconceptions investors hold regarding the backend infrastructure necessary to power the AI economy. His commentary serves as a crucial check for founders and VCs focused on where the true long-term value creation resides.</p>
<div class="lb-trans"><p>Munster 在 CNBC 的《收盘铃》节目中，对当前人工智能交易的状态进行了深刻分析，不仅强调了在广告等面向消费者的应用中的竞争动态，还指出了投资者对推动人工智能经济所需后端基础设施的基本误解。他的评论为专注于真正长期价值创造的创始人和风险投资者提供了重要的警示。</p>
</div><p>The idea that both Google and OpenAI can thrive in the advertising space stems from an observed shift in consumer behavior. As large language models become integrated into daily workflow, consumers are increasingly seeking information through conversational interfaces—whether via a traditional search bar enhanced by AI or a dedicated chatbot. This expansion of information seeking, Munster posits, means the total time spent searching for information is increasing, offering new real estate for monetization. The fact that the interviewer used an LLM to select jewelry for her outfit highlights the immediacy and breadth of this application shift, demonstrating that AI is moving beyond purely technical tasks into consumer commerce and lifestyle curation—areas ripe for contextual advertising.</p>
<div class="lb-trans"><p>谷歌和 OpenAI 都能在广告领域蓬勃发展的想法源于对消费者行为变化的观察。随着大型语言模型逐渐融入日常工作流程，消费者越来越多地通过对话界面寻求信息——无论是通过增强了人工智能的传统搜索框，还是通过专用的聊天机器人。Munster 认为，这种信息寻求的扩展意味着搜索信息所花费的总时间在增加，为货币化提供了新的空间。采访者使用大型语言模型为她的服装选择珠宝的事实突显了这一应用转变的紧迫性和广度，表明人工智能正在超越纯技术任务，进入消费者商业和生活方式策划领域——这些都是适合上下文广告的领域。</p>
</div><p>This broadening application layer relies on an infrastructure buildout that is still in its nascent stages. According to Munster, the AI trade has successfully navigated the first two checkpoints of 2024—positive guidance from TSMC driven by accelerating AI demand, and encouraging commentary from Nvidia at CES. The next critical hurdle, expected in late January and early February, will be the capital expenditure (CapEx) guidance from the hyperscalers (Google, Microsoft, Amazon). Expectations for hyperscaler CapEx growth have already inched up from 30% to 34%, and Munster anticipates they may hit 40% or more, indicating a deep commitment to the necessary hardware foundation.</p>
<div class="lb-trans"><p>这一广泛的应用层依赖于仍处于初期阶段的基础设施建设。根据 Munster 的说法，人工智能交易已经成功通过了 2024 年的前两个检查点——来自 TSMC 的积极指导，受加速的人工智能需求驱动，以及 Nvidia 在 CES 上的鼓舞人心的评论。下一个关键障碍，预计在 1 月底和 2 月初，将是超大规模云服务商（谷歌、微软、亚马逊）的资本支出（CapEx）指导。超大规模云服务商的资本支出增长预期已经从 30% 上调至 34%，Munster 预计可能会达到 40% 或更高，表明对必要硬件基础的深度承诺。</p>
</div><p>Yet, a deeper, more structural misconception persists among investors regarding this infrastructure buildout. The current narrative often centers on training—the expensive, computationally intensive process of building and refining the large language models themselves. Munster argues that this focus misses the forest for the trees, noting that investors are “stuck in the mentality that AI is about training.”</p>
<div class="lb-trans"><p>然而，投资者对这一基础设施建设的更深层次、结构性误解依然存在。当前的叙事往往集中在训练上——构建和完善大型语言模型本身的昂贵且计算密集的过程。Munster 认为，这种关注错失了整体，指出投资者 “陷入了人工智能是关于训练的思维模式。”</p>
</div><p>The long-term economic reality, driven by the sheer volume of daily usage, points overwhelmingly toward inference. Inference is the process of running the trained models to generate results—the actual “thinking” and application of intelligence by the AI. Munster cites Deepwater’s internal research and industry data to underscore the massive scale difference: “That inference market last year in 2025 was five times as big as the training market.” Looking forward, this disparity will grow exponentially.</p>
<div class="lb-trans"><p>长期的经济现实，受到每日使用量的驱动，明显指向推理。推理是运行训练模型以生成结果的过程——人工智能的实际 “思考” 和智能应用。Munster 引用 Deepwater 的内部研究和行业数据来强调巨大的规模差异：“去年 2025 年的推理市场是训练市场的五倍。” 展望未来，这种差距将呈指数增长。</p>
</div><p>The intelligence of AI is not in the training, but in the application. As millions of users, businesses, and specialized agents begin to rely on these models for daily tasks, the computational cycles dedicated to inference will dwarf those dedicated to training. Munster emphasizes the scale of this future demand, stating that “Inference will be hundreds of thousands of times bigger than training; intelligence is in thinking, not in the training.” This disparity suggests that companies focused on optimizing inference—the real-time deployment and scaling of AI—are positioned for explosive growth far beyond those merely focused on model creation.</p>
<div class="lb-trans"><p>人工智能的智能不在于训练，而在于应用。随着数百万用户、企业和专业代理开始依赖这些模型进行日常任务，专用于推理的计算周期将远远超过专用于训练的周期。Munster 强调了未来需求的规模，表示 “推理将比训练大数十万倍；智能在于思考，而不是训练。” 这种差距表明，专注于优化推理——人工智能的实时部署和扩展的公司，远比那些仅专注于模型创建的公司更具爆发性增长潜力。</p>
</div><p>This infrastructure dynamic has immediate implications for market structure. While the “Mag 7” (the largest technology companies) have dominated the initial AI run-up, the sheer scale of the required infrastructure investment, combined with the broadening need for specialized inference chips and services, suggests that the AI opportunity will trickle down. Munster maintains his prediction that small-cap companies focused on the infrastructure and application layers will begin to outperform their large-cap counterparts, driven by strong earnings revisions as the inference market accelerates. The market strength seen recently in companies like AMD, following positive supply chain news, is an early indicator of this broadening participation in the infrastructure supply chain.</p>
<div class="lb-trans"><p>这一基础设施动态对市场结构有直接影响。虽然 “魔法七”（最大的科技公司）主导了初期的人工智能热潮，但所需基础设施投资的庞大规模，加上对专用推理芯片和服务的广泛需求，表明人工智能机会将向下渗透。Munster 坚持他的预测，专注于基础设施和应用层的小型公司将开始超越大型公司，推动强劲的盈利修正，随着推理市场的加速。最近在 AMD 等公司的市场强劲表现，受到积极的供应链消息的推动，是这一基础设施供应链广泛参与的早期指标。</p>
</div><p>For founders and VCs, the takeaway is clear: the AI gold rush is shifting from digging for gold (training) to selling shovels and processing tools (inference). The next phase of massive value creation will be found in the companies that enable the affordable, scalable, and ubiquitous deployment of AI intelligence across every facet of technology and commerce, confirming that the AI infrastructure buildout is far from complete.</p>
<div class="lb-trans"><p>对于创始人和风险投资者来说，结论很明确：人工智能的淘金热正在从挖掘黄金（训练）转向销售铲子和处理工具（推理）。下一阶段的巨大价值创造将出现在那些能够在技术和商业的各个方面实现经济、可扩展和普遍部署人工智能智能的公司，确认人工智能基础设施建设远未完成。</p>
</div>

The AI Infrastructure Buildout is Still Early and Investors Are Stuck on Training

- Gene Munster 认为，Alphabet 与 OpenAI 在 AI 广告业务中可共赢。  
- 他分析了 AI 领域基础设施的重要性及投资者普遍存在的误解。  
- Munster 预计，聚焦推理的公司将迎来更大的增长机会。

人工智能基础设施的建设仍在初期阶段，投资者仍然专注于训练阶段