Ricursive Intelligence 押注 AI 设计芯片以缓解算力危机
Ricursive Intelligence is betting on AI-designed chips to help solve the compute crisis
- 1Ricursive Intelligence 创始人在 TechCrunch Disrupt 介绍用 AI 自动化芯片设计、逐代学习改进的创业思路
- 2创始人称未来五年算力需求至少增长 10000 倍且估计可能偏保守,主张靠更高效芯片而非多建数据中心(创始人口径)
- 3两人此前开发的 AlphaChip 曾生成环形甜甜圈状元件排布,起初令工程师意外,最终被证明成功
- 4创始人称传统流程设计一颗芯片需半年到两年,若缩到一周可为空间、AR、VR 等场景定制芯片(创始人口径)
用 AI 自动化芯片设计被提出为缓解算力缺口的路径,内容以创始人愿景叙述为主,未给出产品、客户或量产证据。
- 影响:算力需求五年至少增 10000 倍的判断指向数据中心成本压力延续;若芯片设计周期真能从半年到两年缩到一周,为细分场景定制芯片在财务上可行;文中未提产品与客户,当前影响偏方向性。
- 参考:可考虑先测算自身未来几年算力需求与数据中心预算的增长曲线;硬件相关团队可评估 AI 辅助芯片设计工具的成熟度,为自有场景定制芯片先算一笔成本账。
- 依据:10000 倍需求、一周设计芯片等数字均出自两位创始人在 TechCrunch Disrupt 的现场发言,属创始人口径,暂无独立验证。
Ricursive Intelligence founders Anna Goldie and Azalia Mirhoseini believe more efficient AI chip design could help solve the compute crisis. The founders took the stage at TechCrunch Disrupt on Wednesday to detail how their startup is developing AI that can design chips, learn from each iteration, and improve subsequent designs, all with the goal of automating and accelerating the chip design process.
“There is this understanding that in the next five years we are going to have at least 10,000x more demand for compute, and that’s somewhat potentially conservative,” Goldie said on stage. “So we don’t want to build 10,000 times more data centers. We think that we need to help by making chips that are more efficient, that use the bandwidth in those data centers more efficiently, that use the wafers and fabs more efficiently, because even if we make [data centers] in space or something, I don’t know, you won’t see the sun anymore.”
While some may be scared by the thought of AI designing its own chips, Goldie argues that letting AI handle chip design will allow humans can focus on developing applications and exploring new possibilities that can expand what they can accomplish with computing.

“I think the way everything is shifting is that humans come bring their great ideas and creative thoughts, and execution is handled by AI,” Goldie said. “And this is happening in chip design, software development, and other places. And that’s something that recursive does for chip design.”
The founders say AI is challenging traditional chip design methods by discovering unconventional layouts that can outperform traditional ones. For example, AlphaChip, the AI system the founders previously developed, produced a circular, “donut-shaped” arrangement of chip components that initially surprised engineers but ultimately proved to be successful.
They explained that they are optimizing entire chips rather than following the traditional approach of partitioning them into dozens of smaller blocks. This approach allows AI to create intricate patterns, including wave-like designs, which the pair said they plan to share more about in the future.
The founders also argued that AI-designed chips will make it possible to create all sorts of chips that might not have been possible before.
“If it takes six months or a year or two years to design a new chip, then you really have to be extremely selective about the chips that you design,” Goldie said. “But if you can design a chip in like a week, and then say for manufacturing, you can design custom chips for all sorts of different types of applications in like space and AR, VR, and you biological computing, and it makes sense financially to do that, and we can be more energy efficient.”
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Aisha is a consumer news reporter at TechCrunch. Prior to joining the publication in 2021, she was a telecom reporter at MobileSyrup. Aisha holds an honours bachelor’s degree from University of Toronto and a master’s degree in journalism from Western University.
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来源:TechCrunch · AI · techcrunch.com