Omen AI 获3100万美元A轮融资,用冷却液监测技术优化数据中心
评分
51精选理由
技术应用场景明确,市场刚需强,融资规模合理,但产品尚处早期阶段,评分75。
AI 点评
「随着AI算力需求激增,数据中心散热成为瓶颈,Omen AI 的冷却液智能监测有望大幅降低能耗与故障率。」
速览
Omen AI 完成3100万美元A轮融资,致力于通过监测芯片冷却液并防止细菌爆发来优化数据中心。其技术可实时分析冷却液成分,预警微生物污染,提升散热效率并降低停机风险,为高密度计算提供可靠保障。
- Omen AI 获得3100万美元A轮融资
- 核心方案:监测冷却液并防止细菌滋生
- 目标:提升数据中心散热效率与可靠性
正文
The AI-driven demand for compute power has data centers looking to squeeze more from every rack of GPUs. One consequence? Bacterial outbreaks.
The liquid for liquid-cooled chips is a mixture of water and a substance that inhibits bacteria growth. To run the chips hotter, data center managers can change the mix to include more water, which absorbs heat better, but leads to nasty contamination that clogs the flow. To solve that, they flush the system, which can mean shutting down a rack for five or six hours at a potential cost of millions of dollars.
Omen AI has a solution: A tiny spectrometer that can monitor that fluid health in real time, spotting bacterial growth before it becomes a massive problem. “You’re not risking huge amounts of downtime because you have no insight into what’s going on chemically,” explains CEO and founder Zach Laberge.
Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave.
Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school. (His father and mother, a former Minister of Education for Ontario, were supportive of his plan to carve his own path.)
After that startup shut down, Laberge started Omen in 2024, with the idea of focusing on fluid systems as the key to enabling construction machinery to be smart enough to know when it needed to be fixed. The idea was to replace the time-consuming process of extracting samples and sending them to a lab with real-time awareness. Besides bacterial growth, the device can spot pumps and pumps wearing out if it sees copper or chromium, or seals if it sees silicon.
Caterpillar dealerships were a key early customer for Omen’s heavy vehicles business, but Cat is also a major supplier of gas-powered turbines and generators to provide on-premises power for data centers. It didn’t take long for Omen to see where the wind was blowing.
“That was kind of the transition,” Laberge told TechCrunch. About six months ago, “a lot of the dealerships were saying, ‘Hey, we’re starting to put sensors on our turbines, can you guys do anything on the building side of things?’”
Omen discovered that those buildings are full of fluid, from their HVAC systems to their chip cooling. Spotting a new, fast-growing group of potential customers, Omen began to focus on data centers.
“It’s rare to see such a young founder who has the respect of established, large corporations in a space that moves a bit more slowly,” said Cory Rellas, a partner at Nava Ventures who sits on Omen’s board. “For Omen in particular, much of our diligence came through our introductions with large customers which quickly validated their approach.”
Omen, which has raised $40 million since its founding in 2024, is working with a dozen data center customers as they build out their offering, including TensorWave, a company building an AI compute cloud on AMD chips.
“The fluid running through these massive systems is a critical variable that most of the industry is flying blind on,” Piotr Tomasik, TensorWave’s president, said in a statement. “Omen … see the future of infrastructure exactly the way we do, better monitoring to optimally support compute customers.”
While many organizations rely on mailing fluid samples to labs for insight, Omen isn’t alone in developing on-premises analytics — Pyxis, an established water-monitoring firm, rolled out its data center coolant monitoring product earlier this month.
The key tech advances that unlocked this approach are recent improvements in both optical technologies and signal processing software. “Hardware is just cheap enough that it makes sense to play at scale, and then signal processing lets us make more sense out of the noise,” Laberge said.
Topics
AI, Exclusive, Fundraising, Nava Ventures, omen ai, StartupsWhen you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
Tim Fernholz
Senior Reporter
Tim Fernholz is a journalist who writes about technology, finance and public policy. He has closely covered the rise of the private space industry and is the author of Rocket Billionaires: Elon Musk, Jeff Bezos and the New Space Race. Formerly, he was a senior reporter at Quartz, the global business news site, for more than a decade, and began his career as a political reporter in Washington, D.C. You can contact or verify outreach from Tim by emailing tim.fernholz@techcrunch.com or via an encrypted message to tim_fernholz.21 on Signal. View Bio
November 4
Boston
Last chance to save up to $190 on TechCrunch Founder Summit. Join 1,000+ founders and VCs at all stages for real-world scaling insights and connections that move the needle.
Savings end June 26, 11:59 p.m. PT.
Most Popular
-
Chevy built an all-American EV truck — why is nobody buying it?
-
Mark Zuckerberg tells staff that AI agents haven’t progressed as quickly as he’d hoped
-
Jersey Mike’s IPO illustrates how bad the AI hype has become
-
After $18B IPO, Bending Spoons founder says success comes from minimizing luck
-
-
-
Flipper Device’s new Busy Bar is a customizable display for productivity
相关条目
89
精选OpenAI研究:AI正在重新定义工作边界
如果你关心AI对实际工作的影响,这篇研究不容错过。它不讨论理论,而是用数据展示ChatGPT用户如何使用AI扩展自己的任务范围,这对HR、管理者以及每个希望提升技能的职场人都有直接启发。
87
精选OpenAI CFO 分享构建 AI 原生财务职能的五大经验
作为编辑,我认为这篇文章的价值在于它来自 AI 领军企业的财务一把手,而非外部咨询机构,因此更具实践参考性。推荐给财务数字化负责人、CFO 以及对 AI 转型感兴趣的从业者阅读。
87
精选智能体AI时代下的科学计算:OpenAI发布新领域报告
这份报告是当前AI agent热潮中最务实的应用分析之一,尤其适合计算生物学和药物研发领域的从业者阅读。它回答了“AI agent除了写代码还能干什么”这个关键问题,直接展示了在复杂科学计算中的生产力提升。
87
精选Real World VoiceEQ:量化语音AI的人类自然度
值得关注的是,这个基准真正聚焦'人类自然度'这个软指标,而不是传统的语音识别率或MOS评分。对从事语音合成、语音克隆或语音交互的团队来说,这是一个非常实用的参考工具,可以快速定位模型在情感、韵律等层面上的不足。不过目前支持语言有限,期待后续扩展。
85
精选OpenAI 推出AI时代记分卡:衡量投资回报的新方法
这篇博文直击当前AI行业的软肋——如何量化投入产出。对于正在或计划大规模部署AI的企业决策者而言,它能帮助避免盲目跟风。我个人建议结合自身业务场景尝试定义“有用工作”,避免指标设计流于形式。
85
精选代理时代如何管理AI投资:聚焦每美元有用功与高价值工作流
这篇文章对正在大规模部署AI代理的企业尤为重要。它提醒我们:单纯盯着API价格下降会陷入误区,真正要管理的是每单位工作的投资回报率。建议CTO和CFO联合阅读,并在内部建立量化评估机制。
85
精选设计能跟上AI的组织:组织延迟成为最大障碍
这篇文章真正值得关注的是提出了一个被忽视的瓶颈:技术再强,组织跟不上也是白搭。对CTO、CIO和转型负责人来说,这是比技术选型更重要的思考框架。
84
精选OpenAI宣布与美国能源部和国家实验室合作,推动前沿AI加速科学发现
这不仅是技术合作,更是AI国家战略的里程碑。对关注AI政策与科学创新的读者,值得细读框架设计。