内容来源：https://techcrunch.com/2025/11/18/hugging-face-ceo-says-were-in-an-llm-bubble-not-an-ai-bubble/

内容总结：

知名人工智能平台Hugging Face联合创始人兼首席执行官克莱门特·德朗格近日公开表示，当前人工智能领域并未出现全面泡沫，而是正经历"大语言模型（LLM）泡沫"，且这个泡沫可能在明年破裂。在周二举行的Axios活动上，这位业界领袖指出，虽然泡沫讨论是当下"价值万亿美元的问题"，但即便LLM泡沫破裂，也不会影响人工智能整体的发展前景。

德朗格强调，以ChatGPT、Gemini等聊天机器人为代表的LLM模型目前获得了过多关注，这种热度难以持续。他认为LLM仅是人工智能的一个子集，在生物、化学、图像、音视频等专业领域的AI应用才刚起步，未来几年将呈现更丰富的发展形态。

针对行业现状，这位企业家犀利指出："当前所有注意力、焦点和资金都集中在通过算力堆砌通用模型的思路上，但现实将证明，未来更需要定制化、专业化的多元模型体系。"他以银行业客服场景为例说明，特定场景无需具备哲学思辨能力的巨型模型，采用更轻量、高效的专用模型反而能实现更优成本效益。

值得注意的是，德朗格坦言LLM泡沫破裂可能对其公司产生局部影响，但强调人工智能产业生态已足够庞大和多元化。相较于部分同行斥资数十亿美元的激进策略，Hugging Face目前仍持有2亿美元融资储备的一半资金，这种注重资本效率的稳健打法，体现了其深耕AI领域15年积累的行业智慧。

在行业普遍焦虑追逐短期利益的氛围中，德朗格表示将坚持长期主义路线，致力于打造具有持续影响力的AI企业。这一表态为火热的人工智能赛道提供了冷静的观察视角。

中文翻译：

Hugging Face联合创始人兼首席执行官克莱门特·德兰格表示，当前我们并非处于人工智能泡沫中，而是身处"大语言模型泡沫"——这个泡沫可能即将破裂。在本周二Axios活动上，这位广受欢迎的人工智能平台兼社区网站的掌舵人认同"泡沫论"是当今"万亿美元级命题"，但他强调即便泡沫破裂，也不会危及人工智能的未来。

德兰格认为，真正被过度关注的是驱动ChatGPT、Gemini等聊天机器人的大语言模型，而这种热度恐难持久。"我们正处在大语言模型泡沫中，这个泡沫或许明年就会破裂。"他解释道，"但就人工智能在生物、化学、图像、音视频等领域的应用而言，大语言模型只是其中组成部分。我认为人工智能才刚刚起步，未来几年将呈现更广阔的发展图景。"

他指出问题核心在于：大语言模型并非万能解决方案，未来更小型化、专业化的模型将获得更多应用场景。"当前所有注意力、资源和资金都集中在'通过海量计算构建通用模型解决全人类问题'的理念上。但现实是，未来数月乃至数年内，我们将看到更多经过定制化、专业化处理的模型集群，它们将针对不同问题提供解决方案。"

以银行客服聊天机器人为例，德兰格阐述道："它不需要探讨生命意义这类哲学命题，对吧？采用更小巧专业的模型不仅成本更低、响应更快，企业还能将其部署在自有基础设施上。我认为这才是人工智能的未来走向。"

这位Hugging Face创始人承认大语言模型泡沫破裂会对其公司产生一定影响，但同时强调人工智能产业规模庞大且已实现多元化发展。这意味着即使某个细分领域（如大语言模型）存在估值过高现象，也不会对整个人工智能领域或其业务造成重大冲击。

此外他透露，Hugging Face已融资4亿美元，目前账上仍留存半数资金。这种审慎的支出策略与当下其他AI公司（特别是大语言模型领域企业）形成鲜明对比。"按人工智能行业标准，我们这种做法可称为盈利模式——毕竟其他企业烧掉的钱不是数亿美元，而是明显达到数十亿量级。"

相较而言，Hugging Face正采取更高效的资本运作方式。"现在很多人急于求成，甚至带着恐慌情绪采取短期策略。我在AI领域深耕15年，见证过多次行业周期。"德兰格补充道，"我们正从中汲取经验，努力打造一家具有长期价值、可持续增长并能造福世界的企业。"

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英文来源：

Hugging Face co-founder and CEO Clem Delangue says we’re not in an AI bubble, but an “LLM bubble” — and it may be poised to pop. At an Axios event on Tuesday, the entrepreneur behind the popular AI platform and community site agreed that bubble talk is today’s “trillion-dollar question,” but said he doesn’t believe AI’s future is at risk if the bubble bursts.
Instead, as Delangue sees it, it’s large language models (LLMs) — like those powering ChatGPT, Gemini, and other chatbots — that are receiving outsized attention, and that attention may not last.
“I think we’re in an LLM bubble, and I think the LLM bubble might be bursting next year,” explained Delangue. “But ‘LLM’ is just a subset of AI when it comes to applying AI to biology, chemistry, image, audio, [and] video. I think we’re at the beginning of it, and we’ll see much more in the next few years,” he noted.
One issue, he argued, is that LLMs aren’t the right solution for everything, and smaller, more specialized models will see increased adoption in the future.
“I think all the attention, all the focus, all the money, is concentrated into this idea that you can build one model through a bunch of compute and that is going to solve all problems for all companies and all people,” said Delangue. “I think the reality is that you’ll see in the next few months, next few years, kind of like a multiplicity of models that are more customized, specialized, that are going to solve different problems.”
As an example, he suggested the use case of a banking customer chatbot.
“You don’t need it to tell you about the meaning of life, right? You can use a smaller, more specialized model that is going to be cheaper, that is going to be faster, that maybe you’re going to be able to run on your infrastructure as an enterprise, and I think that is the future of AI,” Delangue pointed out.
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The Hugging Face founder admitted that an LLM bubble bursting could impact his company to some extent, but noted that the AI industry is so big that it’s already diversified. That means even if a portion of the industry is overvalued, like LLMs, it’s not going to have a massive impact on the AI field itself or his business.
Plus, he noted, Hugging Face still has half of the $400 million it has raised in the bank. This cautious approach to spending represents a different strategy from what other AI companies are doing these days, especially in the LLM space.
“In AI standards, that’s called profitability because the other guys — it’s not hundreds of millions that they’re spending. It’s obviously billions of dollars,” he said.
By comparison, Hugging Face is taking a more capital-efficient approach.
“I think a lot of people right now are rushing — or maybe even panicking — and taking a really short-term approach to things. I’ve been in AI for 15 years now, so I’ve seen some of the cycles,” Delangue added. “And so we’re learning from that and trying to build a long-term, sustainable, impactful company for the world.”