AI模型的能力不断提升，但每一次飞跃都需要更多算力、能源和基础设施。这种发展态势还能持续多久？
AI models keep getting more capable. But every leap forward demands more compute, energy, and infrastructure. How far can that continue?
Cerebras Systems过去十年一直在挑战AI计算的一项基本假设：日益强大的AI必须依赖传统芯片架构。该公司围绕晶圆级计算构建技术方案，如今通过本地部署系统和云平台提供AI算力。
Cerebras Systems has spent the past decade challenging a basic assumption behind AI computing: that increasingly powerful AI must depend on conventional chip architectures. The company built its approach around wafer-scale computing and today provides AI compute through on-premise systems and its cloud platform.
TechCrunch Disrupt 2026上，Cerebras Systems首席执行官兼联合创始人安德鲁·费尔曼将登上Disrupt舞台，探讨“AI能否持续扩展？”他将探讨对算力、能源和基础设施日益增长的需求、Cerebras如何以不同方式应对这些限制，以及如果当前AI硬件触及极限，未来将会如何。想了解哪些因素可能决定AI能扩展到何种程度？
TechCrunch Disrupt 2026 Cerebras Systems CEO and co-founder Andrew Feldman will take the Disrupt Stage for “Can AI Keep Scaling? ” He’ll explore the growing demand for compute, energy, and infrastructure, how Cerebras is approaching those constraints differently, and what comes next if today’s AI hardware reaches its limits. Want to understand what could determine how far AI can scale?
购买Disrupt门票，聆听一位解决AI扩展难题的创始人分享经验。带一位联合创始人、同事或同行参会，可享五折优惠。Cerebras挑战传统AI芯片。安德鲁·费尔曼于2015年联合创立Cerebras，此前曾多年围绕计算基础设施创办公司。创立Cerebras之前，他联合创立并领导了高效能微服务器初创公司SeaMicro，AMD于2012年将其收购。更早之前，他曾在Force10 Networks和Riverstone Networks担任领导职务。
Secure your Disrupt ticket to hear from a founder solving the AI scale challenge. Bring a co-founder, colleague, or peer at 50% off Cerebras challenges the conventional AI chip Andrew Feldman co-founded Cerebras in 2015 after years of building companies around computing infrastructure. Before Cerebras, he co-founded and led energy-efficient microserver startup SeaMicro, which AMD acquired in 2012. Earlier, he held leadership roles at Force10 Networks and Riverstone Networks.
如今，随着AI算力需求不断增长，Cerebras正在扩大这一方案的应用规模。公司在5月的IPO中募资55亿美元，并与OpenAI签署多年协议，计划从2026年至2028年部署总功率达750兆瓦的Cerebras系统。8月，Cerebras推出了CS-4，这是其最新一代晶圆级AI基础设施。
At Cerebras, Feldman and his co-founders took on a problem long considered impractical: bringing wafer-scale computing to market. Rather than cutting a silicon wafer into individual chips, Cerebras developed a processor built on the wafer itself, an architecture designed specifically for demanding AI workloads. Now Cerebras is scaling that approach as demand for AI compute grows.
像 Cerebras 这样的公司能否满足人工智能日益增长的计算需求？其背后的基础设施能否跟上这种发展的步伐？
The company raised $5.5 billion in its May IPO and signed a multiyear agreement with OpenAI to deploy 750 megawatts of Cerebras systems from 2026 through 2028.
现在购买 Disrupt 的产品可享受 50% 的折扣；同时，您还可以聆听一位企业家分享他的经验——他多年来一直在探索构建人工智能硬件的新方法。
In August, Cerebras introduced CS-4, the latest generation of its wafer-scale AI infrastructure. Can approaches like Cerebras’ deliver the compute increasingly powerful AI demands — and can the infrastructure behind them keep up?
要实现人工智能的规模化发展，就必须相应地扩展其背后的基础设施。仅仅拥有更强大的处理器是远远不够的；这些系统还需要数据中心、电力、冷却系统以及相应的制造能力。
Secure your Disrupt pass get a second at 50% off, and hear from an entrepreneur who has spent more than a decade betting on a different way to build AI hardware. Scaling AI means scaling the infrastructure behind it More powerful processors alone don’t solve the scaling problem. Those systems need data centers, electricity, cooling, and manufacturing capacity.
Cerebras 已经在应对这一挑战。该公司表示，到 2027 年底，他们将拥有超过 600 兆瓦的数据中心处理能力（这些资源要么已经投入使用，要么已签订采购合同）；同时，他们在 2026 年将制造能力提高了十倍以上。该公司还计划在今年启用其首个欧洲数据中心，并在 2027 年底前将那里的数据中心处理能力提升至 200 兆瓦。
Cerebras is already confronting that challenge. In August, the company reported more than 600 megawatts of data center capacity live or under contract for delivery by the end of 2027 and said it was increasing manufacturing capacity more than tenfold during 2026. It also plans to bring its first European data center capacity online this year and expand to 200 megawatts there by the end of 2027.
人工智能的规模化发展不仅仅意味着设计更快的处理器，还需要足够的物理基础设施来支持这些计算需求。
Scaling AI isn’t solely about designing a faster processor. It requires enough physical infrastructure to put that compute to work.
对于那些在人工智能基础设施方面做出决策的创始人、投资者和技术领导者来说，Feldman 可以帮助他们了解以下关键问题：计算需求的未来发展方向、支持这些需求所需的资源，以及当前硬件技术可能面临的局限性。
For founders, investors, and technology leaders making decisions around AI infrastructure, Feldman can put those constraints into context: where compute demand is heading, what it takes to support it, and where today’s hardware could hit its limits.
欢迎您带着同事或同行一起来参加 Disrupt 活动，了解当前硬件技术达到极限后会发生什么。Cerebras 在当今人工智能基础设施蓬勃发展之前就已经开始探索“晶圆级计算”技术（即利用整个晶圆进行计算）；如今，随着人工智能需求的加速增长，该公司正在不断扩展自身的计算和制造能力。
for Disrupt to hear what Cerebras’ experience can tell us about the next phase of AI scale. Bring a co-worker, colleague, or peer at Learn what happens when today’s hardware reaches its limits at Disrupt Cerebras pursued wafer-scale computing long before today’s AI infrastructure boom and is now scaling its computing and manufacturing capacity as demand for AI accelerates.
在 Disrupt 大会上，Feldman 将探讨算力、能源和基础设施需求增长对人工智能未来意味着什么，以及如果传统硬件无法继续跟上步伐，可能会发生什么。对于所有正在开发、融资或部署人工智能的人来说，这将是一次难得的机会，可以听到一位创始人介绍应对这个行业最大限制因素之一的另一种方案。
At Disrupt, Feldman will explore what growing demand for compute, energy, and infrastructure means for AI’s future and what could happen if conventional hardware can no longer keep pace. For anyone building, funding, or deploying AI, it’s a chance to hear from a founder testing a different approach to one of the industry’s biggest constraints.
他的场次只是 Disrupt 大会200多场活动之一。大会涵盖六个行业分会、圆桌讨论和小组交流，将于10月13日至15日在旧金山莫斯孔西中心举行。预计将有超过1万名创始人、投资人、运营者和科技领袖参会，250多名演讲嘉宾和300多家参展
His session is one of 200+ sessions across six industry stages, roundtables, and breakouts at Disrupt, taking place October 13-15 at Moscone West in San Francisco More than 10,000 founders, investors, operators, and tech leaders are expected, along with 250+ speakers 300+ exhibiting startups Beyond the agenda, matchmaking, dealmaking, and networking create opportunities to connect with the founders, investors, and builders shaping what comes next. to learn how long AI can keep scaling — and what must change for it to continue. Lean in on this session with your co-founder, partner, or peer at