内容来源：https://www.geekwire.com/2025/is-there-an-ai-bubble-investors-sound-off-on-risks-and-opportunities-for-tech-startups-in-2026/

内容总结：

近期，关于人工智能领域是否存在投资泡沫的讨论持续升温。针对这一问题，多家美国西雅图地区的风险投资机构合伙人分享了他们的看法。尽管观点存在细节差异，但投资者普遍认为当前AI市场存在局部过热，尤其是早期初创企业的估值明显脱离实际业务表现，不过整体并未形成灾难性泡沫，AI技术已在创造真实价值。

市场现状：局部过热与真实价值并存
多数投资者指出，过热现象在私募市场尤为明显，特别是在种子轮和A轮融资阶段，资本追逐叙事而忽视基本面的情况突出。然而，在公开市场，头部AI公司仍能以强劲的盈利和增长支撑估值。富达投资合伙人指出，AI的长期影响可能需10至20年才能完全显现，短期内公开市场可能出现回调，但这属于过热后的正常调整。

风险与机遇：资本错配与历史性窗口
部分投资者提醒，当前全球尤其是数据中心等基础设施领域的资本投入可能存在错配。但与此前加密货币等概念不同，大语言模型等AI技术已展现出切实能力，将成为未来软件与知识工作的核心工具。当前企业软件预算正经历前所未有的调整期，为从底层构建AI原生应用的新公司提供了取代传统厂商的巨大机会。

给创业者的建议：聚焦实际问题，构建可持续业务
面对市场环境，投资者一致建议创业者应忽略炒作噪音，专注于解决真实客户问题，并通过年度或多年合同建立可持续收入。创业者应深入客户技术栈打造差异化产品，注重客户投资回报与单位经济效益，利用AI实现效率提升而非盲目烧钱。此外，当前实验与产品构建成本已大幅降低，小型团队只要聚焦高效执行，便有机会构建下一代坚实的AI企业。

总体而言，风险投资界认为AI正处于一场真实技术变革的早期阶段，市场在经历周期性调整后将回归理性，能够创造可衡量价值的企业将最终胜出。

中文翻译：

人工智能正吸引着前所未有的资本与关注。关于所谓"AI泡沫"的质疑也日益增多：是否太多初创企业追逐着相同的创意？估值是否已脱离实际应用水平？巨额投资终将获得回报——还是泡沫破裂？

GeekWire采访了西雅图地区的多位风险投资人，探讨他们是否认为存在AI泡沫，以及初创企业在规划2026年发展时应如何准备。

综合来看，投资者们描绘出这样一幅图景：市场局部过热，但远未崩盘。他们清晰地看到AI领域的过度繁荣——特别是在早期私营企业领域，估值常常超越实际业务进展。但多数人并不认同灾难性泡沫的说法，普遍认为这项技术已在创造真实价值。

具体观点各有侧重：有人认为数据中心建设存在最大泡沫；另一些人指出某些靠概念驱动的初创企业虽无实际客户基础却获得超高估值。有投资者认为AI的全面影响需要10到20年才能显现，也有投资人看到企业正在重新规划软件支出，这使传统供应商面临挑战，其中蕴藏着即时机遇。

他们对创业者的建议是：远离炒作喧嚣，聚焦真实客户痛点，建立可持续营收与高效商业模式，并为市场降温做好准备。

以下为完整访谈内容：

玛德罗纳投资公司合伙人 张诗敏（Sabrina Albert Wu）
"AI市场部分领域确实存在明显泡沫，尤其体现在估值远超基本面的早期私营企业，这符合经典'泡沫'定义。但在公开市场，顶尖AI企业正以超常盈利和增长支撑估值，因此与传统泡沫现象不同。

最显著的过热集中在私募市场，特别是种子轮和A轮融资阶段，众多投资者争相提前布局AI赛道。这导致资本追逐那些业务基础薄弱、估值却预设需要多年经营才能实现目标的初创企业。

初创企业应尽早夯实商业基础：通过年度或多年合约建立可循环营收，解决真实客户问题，通过深度融入客户技术栈构建差异化优势，形成真正的产品与公司飞轮效应。长期成功源于持续交付可衡量的价值与可防御的增长。"

富思创投管理合伙人 卡梅隆·博鲁曼德（Cameron Borumand）
"多重因素正在共同作用。AI是真正具有变革性的新兴技术，长期来看将彻底重塑几乎所有行业的运作模式。同时历史告诉我们，新技术往往短期被高估、长期被低估。AI最深刻、最完整的实现可能仍需10到20年。

短期（未来几年）我预计公开市场会出现回调，因为投资者逐渐认识到AI实现真正的'企业级就绪'需要时间。这并非灾难信号——只是纳斯达克指数年均21%的增长难以持续，可能逐步回归30年来约10%的平均水平。经历几次显著回调后，专家们难免宣称AI被过度炒作。实际上，这只是公开市场经历AI驱动非凡增长后的正常化调整。

后期私募市场会出现部分被过度炒作的企业——每个繁荣周期皆如此。赢家规模将空前庞大，败者的损失也将前所未有。当像Anthropic这样的企业从10亿美元营收增长至2025年预计90亿美元时，显然AI已在创造真实重大的全球影响。

对初创企业而言，当下正是最佳建设时机：并购市场复苏、客户预算充足、人才渴望参与创新项目。但需注意市场杂音过多，建议深耕细分领域，聚焦核心客户痛点。目前增长主要集中于基础设施层——未来几年将是新一代AI应用的主场。"

创始人工厂创始管理合伙人 克里斯·德沃尔（Chris DeVore）
"是的，全球投入AI领域（特别是数据中心建设）的大量资本几乎必然存在错配。具体到初创企业，除少数公认赢家（OpenAI、Anthropic、Cursor）外，问题主要不在于资金过剩，而在于融资估值与企业实际现金流及盈利潜力的脱节。

但不同于近期某些泡沫（加密货币、元宇宙等），这次澡盆里确实有婴儿。大语言模型即使以当前发展水平已是卓越工具，即便金融市场回归理性后，它们仍将是软件开发与知识工作的核心。

当前创始人与投资者面临的挑战在于：如何做出十年后仍显明智的决策，而非仅适应当下。是否存在将大语言模型应用于经济细分领域的方法，既能创造持久商业价值，又不会因短期资本涌入导致过度投资或零和竞争？唯一替代策略是在资本战争中押注赢家并按市场价收购资产，但历史表明即便最优秀的玩家胜算也极低。

这个时代的成功法则与以往并无不同：选择你比任何人都更理解的客户群体，深度接触这些客户，发掘那些过去因难度或成本过高未能解决、如今能通过大语言模型独特破解的痛点，快速迭代构建产品并向客户证明价值，并尽可能保持这种推进与学习节奏。

这听起来简单，但真正做到的创始团队少之又少——这正是创业如此艰难又如此迷人的原因。"

托拉资本董事总经理 希拉·古拉蒂（Sheila Gulati）
"总体而言，我认为当前并未处于AI泡沫中。类似担忧在十五年前我们推出Azure平台时就已存在，当时人们担心会陷入零利润业务的竞争。

如今大规模的AI基础设施建设将塑造驱动实际性能的运营软件层——计算编排、数据管道、内存系统与大规模推理效率。价值正朝着为企业工作流封装部署智能的方向转移。

企业软件初创公司应定位在持续扩张的总目标市场中，提供完整的端到端解决方案以及人机协作的新工作模式。成功的初创企业既要覆盖不断增长的IT市场，也要把握劳动力市场的部分经济价值。

我们看到CIO预算正经历前所未有的灵活调整。根深蒂固的应用体系现在可以转向那些从零构建的AI原生新玩家。市场机遇空前广阔，企业应致力于打造新的巨头，而非满足于做小型功能公司。"

解锁创投联合创始合伙人 刘安迪（Andy Liu）
"是的，我们正处于AI泡沫中，但形式与多数人想象不同。

资本与估值已严重脱离基本面，特别是对那些缺乏明确客户需求、持久差异化优势或可信盈利路径的企业。我们观察到概念驱动型AI公司与价值驱动型AI公司间的鸿沟日益扩大——前者将'AI'主要作为市场定位手段，后者则运用技术为客户提供可衡量、可复现的价值。

泡沫在早期及成长期最为明显，AI叙事能力可暂时替代业务进展，从而获得高估值融资。本轮周期将孕育出部分优秀企业，但许多初创公司无法达到预期增长，将面临重大估值下调、资本重组或倒闭。

对于规划2026年的创始人，我的建议很直接：

• 建立真实业务，而非精美PPT。如今产品可在融资前快速构建并产生实际收入
• 优先关注运营效率、客户投资回报率与单位经济效益
• 运用AI创造真实杠杆效应，而非作为烧钱的借口

2026年将是绝佳的创业时机。实验与产品构建成本已大幅降低，创始人不再需要学历凭证（计算机学位或MBA）就能创造真实产品与收入。新一代可持续发展的AI企业将由小型团队构建，他们更关注高效执行而非市场炒作。我们非常期待看到更多团队在未来一年打造出卓越产品。"

突破者基金合伙人 安妮·卢辛格（Annie Luchsinger）
"在我看来，当前现象与其说是AI泡沫，不如说是围绕真正变革性平台迁移展开的经典风险投资周期。风险投资始终在与重大技术变革（云计算、移动互联、社交网络）共同适应新常态，而AI是我们迄今所见演进最快的领域。

本轮特殊之处在于速度、规模与资本可获得性。AI的采用速度与规模远超以往平台迁移，同时私募市场资本已达历史高位。当这些力量相互碰撞时，定价机制、时间线与投资者行为自然随之演变。

资本领先于基本面并非新鲜事。市场会出现洗牌，但这不意味着底层价值创造没有发生。拥有真实技术、实际分销渠道与真实客户的企业终将穿越周期。"

英文来源：

AI has attracted unprecedented levels of capital and attention. And questions are growing about the so-called AI bubble: Are too many startups chasing the same ideas? Are valuations running ahead of real adoption? And will all this investment pay off — or pop?
GeekWire polled a handful of Seattle-area venture capitalists about whether they think an AI bubble exists, and how startups should prepare as they plan for 2026.
Taken together, the investors paint a picture of a market that is overheated in places, but far from broken. They see clear signs of excess in AI — especially in early-stage private companies where valuations often outpace real traction. But they largely reject the idea of a catastrophic bubble, and most argue that the technology itself is already delivering real value.
They differ on the details: Some see the biggest excess in data center buildouts. Others point to narrative-driven startups raising at huge valuations without real customer traction. One investor puts AI’s full impact 10 to 20 years out. Another sees immediate opportunity as companies rethink their software spending, making longtime vendors vulnerable.
Their advice to startup founders: ignore the hype, focus on real customer problems, build durable revenue and efficient businesses, and be ready for some market cooling.
Read their full responses below.
Sabrina Albert (Wu), partner at Madrona
“There’s clear froth in parts of the AI market, especially in early-stage private valuations where companies are priced well ahead of fundamentals, which fits a classic ‘bubble’ definition. In the public markets, the strongest AI companies are backing valuations with outsized earnings and growth, so it doesn’t look like a traditional bubble there.
The most pronounced exuberance is in the private markets, particularly at seed and Series A, where many investors are trying to get in earlier on AI exposure. As a result, capital is chasing startups with limited traction and valuations that price in outcomes that may take years of execution to justify.
Startups should focus on durable business fundamentals early on. Build repeatable revenue through annual or multi-year contracts, solve real customer problems, and differentiate by integrating deeply into the customer tech stack to create real product and company flywheels. Long-term success comes from delivering measurable value and defensible growth over time.”
Cameron Borumand, general partner at Fuse
“Many factors are at play here. You have a new and genuinely transformative technology in AI. Over the long term, it will radically reshape how nearly every industry operates. At the same time, history tells us that new technologies tend to be overestimated in the short term and underestimated in the long term. The most profound, fully realized impacts of AI may still be 10-to-20 years away.
In the near term (the next few years), I expect some pullback in the public markets as investors come to terms with the fact that true ‘enterprise readiness’ for AI will take time. This doesn’t suggest anything catastrophic — just that the roughly 21 percent year-over-year growth we’ve seen in the Nasdaq is unlikely to be sustainable and may revert closer to the 30-year average of around 10 percent. After a few meaningful pullbacks, pundits will inevitably claim that AI is overhyped. In reality, this would simply represent a normalization after an extraordinary, AI-fueled run in the public markets.
Late-stage private markets will see some overly hyped companies — this happens in every boom cycle. The winners will be bigger than ever, but the losses will also be bigger than ever. When you have companies like Anthropic growing from $1 billion to a projected $9 billion of revenue in 2025, it’s clear that AI is already delivering real, material impact in the world.
For startups, there’s no better time to be building than now. M&A markets are back, customers have budget, and talent wants to work on interesting projects. With that said, there is a lot of noise, so it’s best to go deep and really focus on a core customer problem. Most of the growth we’ve seen to date is in the infrastructure layer — the next few years will be about the next generation of AI-powered applications.”
Chris DeVore, founding managing partner at Founders’ Co-op
“Yes, a significant amount of capital being deployed globally in AI (and particularly in the data center buildout) is almost certainly being misallocated. Specifically in startups, outside a few presumed winners (OpenAI, Anthropic, Cursor), the concern is less overcapitalization and more the prices at which financings are being done relative to the actual cash flows and margin potential of the companies being financed.
That said, unlike some recent bubbles I can think of (crypto, metaverse, etc.) there are actual babies in the bathwater this time. LLMs are remarkably capable tools even at their current state of development, and will remain core to many software development and knowledge work tasks long after rationality has returned to the financial landscape.
The founder and investor challenge in moments like the current one is how to make decisions that will look smart ten years from now, not just in the current moment. Are there ways to apply LLMs to create durable business value in segments of the economy that are not likely to be overcapitalized or competed to zero by the near-term flood of dollars? The only alternative strategy is to try to pick winners in the capital wars and pay whatever the market demands for those assets, but history suggests that’s a very low odds proposition for even the best players.
The recipe for success in times like this is not that different from any other time: pick a customer segment that you understand better than anyone else, engage deeply with those customers to understand what problems you can uniquely solve with LLMs that were too hard or expensive to solve previously, build quickly and iteratively to show value to those customers, and maintain that pace of shipping and learning for as long as you can.
That may sound simple, but it’s remarkable how few founding teams are able to pull it off, and that why startups are so hard, and so fun.”
Sheila Gulati, managing director at Tola Capital
“Broadly, I don’t think we’re in an AI bubble right now. Similar concerns existed when we launched the Azure platform about fifteen years ago. Back then, people were initially worried about racing to a zero-margin business.
Today’s massive AI infrastructure buildouts will shape the operational software layers that drive real-world performance — compute orchestration, data pipelines, memory systems, and large-scale inference efficiency. Value is shifting toward packaging and deploying intelligence across enterprise workflows.
Enterprise software startups should position themselves in the growing TAM of delivering full, end-to-end solutions and new ways of doing things where humans collaborate with AI agents. Winning startups will encompass both the growing IT TAM and economics of a portion of the labor market as well.
We are now seeing unprecedented malleability of CIO budgets. The deeply entrenched application stack can now shift to new players which are built with AI from the ground up. The market opportunity is massive, and companies should set their sights on building the new megacaps, not minor feature companies.”
Andy Liu, co-founding partner at Unlock Venture Partners
“Yes, we are in an AI bubble, but not in the way most people think.
Capital and valuations are running well ahead of fundamentals, particularly for companies without clear customer pull, durable differentiation, or credible/reasonable paths to profitability. We’re seeing a growing gap between narrative-driven AI companies where ‘AI’ is largely a positioning exercise, and value-driven AI companies that use the technology to deliver measurable, repeatable value for customers.
The bubble seems most pronounced at the early and growth stages where AI storytelling can temporarily substitute for traction and raise capital at lofty valuations. Some strong companies will emerge from this cycle, but there will be meaningful drawdowns, recaps, or shutdowns as many startups fail to grow into those expectations.
Looking ahead to 2026, my advice to founders is straightforward:

• Build real businesses, not decks. Products today can be built quickly with real revenue before raising capital.
• Prioritize efficiency, customer ROI, and unit economics.
• Use AI to create real leverage, not excuses for burning capital.
  2026 is going to be an incredible moment to build. The cost of experimentation and building products has collapsed, and founders no longer need educational credentials (CS degrees or an MBA) to create real products and revenue. The next generation of durable AI companies will be built by small teams who focus less on hype and more on efficient execution. We’re definitely excited to see more teams building incredible products this upcoming year.”
  Annie Luchsinger, partner at Breakers
  “From my perspective, what we’re seeing is less an AI bubble and more a classic venture cycle playing out around a genuinely transformative platform shift. Venture has always adapted to new normals alongside major technology inflections (cloud, mobile, social), and AI is the fastest-moving one we’ve seen to date.
  The difference this time is speed, scale, and capital availability. AI adoption is happening at a faster clip and at a much larger scale than prior platform shifts, all while private-market capital has reached historic highs. As those forces collide, pricing, timelines, and investor behavior evolve.
  Capital moving ahead of fundamentals is not new. There will be some shakeouts, but that doesn’t mean underlying value creation isn’t happening. Companies with real technology, real distribution, and real customers will endure.”