Open-weight AI companies are the Valley’s hottest acquisition targets
Open-weight AI companies are the Valley’s hottest acquisition targets
开源权重 AI 公司成为硅谷最热门的收购目标
Everyone’s waiting for Nvidia to confirm this week’s most interesting tech deal: A reported $13 billion acquisition of Hugging Face, a platform for sharing open-weight AI models and benchmarks. Now best known as the target for a team of reward-hacking OpenAI agents, Hugging Face is at the center of the ecosystem of developers building and deploying LLMs that aren’t owned by frontier labs. Think of it as a kind of GitHub for the AI era. 所有人都在等待英伟达（Nvidia）确认本周最引人注目的科技交易：据报道，英伟达将以 130 亿美元收购 Hugging Face，这是一个用于共享开源权重 AI 模型和基准测试的平台。Hugging Face 目前最出名的是曾遭到 OpenAI 智能体团队的“奖励黑客”攻击，它处于开发者生态系统的中心，这些开发者正在构建和部署不属于前沿实验室的大型语言模型（LLM）。你可以把它看作是 AI 时代的 GitHub。
Rumors of that deal come after Nvidia struck a $6 billion agreement with Poolside, an open-weight model builder, that will see most of its employees move to the chip-making giant. And two weeks ago, Stripe acquired OpenRouter, the top provider of open-weight models to businesses, for more than $7 billion. That’s a lot of capital pouring into a sector based on giving stuff away, and it reflects the latest trends in the AI sector. 在这一收购传闻之前，英伟达已与开源权重模型构建商 Poolside 达成了一项 60 亿美元的协议，该协议将使 Poolside 的大部分员工转投这家芯片制造巨头。两周前，Stripe 以超过 70 亿美元的价格收购了 OpenRouter，这是为企业提供开源权重模型的顶级供应商。大量资本涌入一个以“免费提供产品”为基础的行业，这反映了 AI 领域的最新趋势。
For Nvidia, there’s a need to avoid further dependence on its deals with the major hyperscalers and frontier labs. That’s particularly the case when major AI model builders like OpenAI and Google are also building their own inference chips, like OpenAI’s Jalapeño, whose capabilities were announced this week. If model builders are making chips, Nvidia wants a chunk of the model-making business. 对于英伟达来说，它需要避免进一步依赖与大型超大规模云服务商和前沿实验室的交易。尤其是当 OpenAI 和谷歌等主要 AI 模型构建商也在开发自己的推理芯片（例如本周宣布其性能的 OpenAI Jalapeño 芯片）时，情况更是如此。如果模型构建商开始制造芯片，英伟达也想在模型制造业务中分一杯羹。
Nvidia already builds its own Nemotron family of open-weight models, but their uptake hasn’t been huge. By taking control of the largest U.S. developer space for open models, the company will have access to a mass of users it can drive to its chips and standards. There are also growing questions about the cost of AI inference, which has companies exploring cheaper models built by Chinese companies like Moonshot, DeepSeek, and Alibaba. 英伟达已经构建了自己的 Nemotron 系列开源权重模型，但其采用率并不高。通过控制美国最大的开源模型开发者空间，英伟达将能够接触到大量用户，并将他们引导至其芯片和标准体系中。此外，关于 AI 推理成本的质疑声日益高涨，这促使企业开始探索由月之暗面（Moonshot）、深度求索（DeepSeek）和阿里巴巴等中国公司构建的更廉价模型。
Right now, adoption is relatively small but growing — just 6% of companies use open-weight models, according to a survey of spending data by Ramp, or just 2% of software engineers measured by Jellyfish, which makes tools for developers. Nik Albarran, the AI product lead at Jellyfish, told TechCrunch that open-weight models are primarily used by companies whose products rely on repeated inference workloads, like those providing customer service chats. Because these are high-volume tasks with a lot of repetition, an open-weight model can be tuned to answer the questions cheaply. 目前，开源权重模型的采用率虽然相对较小，但正在增长。根据 Ramp 对支出数据的调查，仅有 6% 的公司使用开源权重模型；而根据为开发者制作工具的 Jellyfish 的统计，仅有 2% 的软件工程师在使用。Jellyfish 的 AI 产品负责人 Nik Albarran 告诉 TechCrunch，开源权重模型主要被那些产品依赖重复推理工作负载的公司使用，例如提供客户服务聊天机器人的公司。由于这些任务量大且重复性高，开源权重模型可以通过微调以低成本回答问题。
That’s certainly how Stripe has framed its OpenRouter acquisition. “Tokens are the central currency for companies building with AI, and it’s clear that the real-world economic potential will depend on making good use of scarce compute resources,” Patrick Collison, Stripe’s co-founder and CEO, said in a statement. Stripe 对其收购 OpenRouter 的解释正是如此。Stripe 联合创始人兼首席执行官 Patrick Collison 在一份声明中表示：“Token 是 AI 构建公司的核心货币，显而易见，现实世界的经济潜力将取决于如何有效利用稀缺的计算资源。”
For coding and agentic tasks, however, varying requests and more reasoning mean that frontier models often win out, in part because the proprietary labs provide easier access, and in some cases a token subsidy. Albarran says that as companies dial in AI workflows, it will be easier to turn to open models. Still, the main reason companies look to those models now is for control and configurability, not because of spending concerns. 然而，对于编程和智能体任务，由于请求多变且需要更多推理能力，前沿模型往往更胜一筹，部分原因是专有实验室提供了更便捷的访问方式，在某些情况下还提供 Token 补贴。Albarran 表示，随着公司不断优化 AI 工作流程，转向开源模型将变得更加容易。尽管如此，目前公司寻求这些模型的主要原因仍是为了控制权和可配置性，而非出于支出方面的考虑。
“There are not many companies where that is the case yet … [but] if the prices continue to go up from the frontier labs, more and more companies will be forced to at least consider it,” Albarran told TechCrunch. “When your AI-driven workflows are much more mature, that’s when it makes sense to invest in self-hosting models.” “目前还没有多少公司达到这种程度……但如果前沿实验室的价格继续上涨，越来越多的公司将被迫至少考虑这一点，”Albarran 告诉 TechCrunch。“当你的 AI 驱动工作流程足够成熟时，投资自托管模型才是有意义的。”
Lin Qiao is the CEO of Fireworks, a leading open-weight models router and host for corporate users that is often discussed as a potential acquisition for a tech giant. Qiao says her company processes 40 trillion tokens a day, more than either of Gemini’s or OpenAI’s APIs. Fireworks’ bet is on model diversity: As LLMs proliferate and improve, it will be easier for companies to train them specifically for their needs. Lin Qiao 是 Fireworks 的首席执行官，这是一家领先的开源权重模型路由和托管服务商，常被视为科技巨头的潜在收购目标。Qiao 表示，她的公司每天处理 40 万亿个 Token，超过了 Gemini 或 OpenAI 的 API 处理量。Fireworks 的赌注在于模型的多样性：随着大型语言模型的激增和改进，公司将更容易针对自身需求进行模型训练。
“Every single app company should consider hiring an in-house researcher,” she told TechCrunch last week. “They can use their product and product data to build their own model. The future is actually specialized intelligence. Literally, every single company should have their own model per use case, and that will happen automatically.” “每一家应用公司都应该考虑聘请内部研究人员，”她上周告诉 TechCrunch。“他们可以利用自己的产品和产品数据来构建自己的模型。未来实际上是专业化智能的时代。从字面上看，每一家公司都应该为每个用例拥有自己的模型，而这将会自动发生。”
It’s easy to forget how early we are in the development of AI as a tool and a business. The dominance of OpenAI and Anthropic, however, isn’t inevitable. As the tech giants look to hedge their bets on the biggest labs, the allure of open technology is proving tough to resist. 我们很容易忘记，AI 作为一种工具和商业模式的发展还处于非常早期的阶段。然而，OpenAI 和 Anthropic 的主导地位并非不可动摇。随着科技巨头寻求对冲其在大型实验室上的投资风险，开源技术的吸引力正变得难以抗拒。