Andy Elder和Arjun Mathur都是斯坦福的室友，两人在Palantir度过十年前都曾学习过计算机科学。 长老领导一个由800人组成的团队为美国国防和情报机构制造软件的产品设计，而马图尔则担任公司商业实践的高级副总裁。 当朋友们决定去年初建立起步器时，他们无疑会一起行动。
尽管在建筑方面没有直接的背景，但长老和马图尔对一个日益严重的问题感到困扰：随着开发成本的飙升，今天在美国建造的建筑比几十年前少。
Mathur告诉TechCrunch说
他们相信AI能提升整个建筑、工程和建筑(AEC)行业的生产力，梦已经从隐蔽中出现，由安德烈森·霍罗维茨(Andreessen Horowitz)牵头的1700万美元的种子资金，与包括SV Angel,XYZ，和Zigg Capital在内的其他支持者一起。 启动旨在替换Autodesk的AutoCAD和Revit等繁琐，过时的AEC工具。
如果你坐在建筑师或结构工程师旁边，你会注意到他们仍然在“绘制”他们设计的每一块关于遗留软件的逐个部件，即Mathur 称。 “大赦国际实际上在这一方程式中没有任何作用。”
ChatGPT这样的标准LLM可以生成建筑物的图像，但结果不会是结构上的声音。 相形之下，梦想是基于真正的物理和建筑标准。 正如LLMs从文本中学习的那样，Dream的世界模型也接受了从全美国许可证档案馆收集的超过1亿平方英尺的建筑数据的培训。
“我们的模型实际上理解这些墙里有什么。 哪些材料被使用，梁是用什么制成的，它们能支撑多少重量，"长老称。
启动将它的方法称为“结构cognition”，因为其模型产生结构精确的设计，其中考虑到管道和HVAC等内部建筑元素。
Dream已经开始将其产品出售给AEC的专业人士和开发者，根据创始人的说法，他们利用这个模型来构思建筑设计并估算总体成本。 该平台已经部署在德克萨斯州和加利福尼亚州的10个活跃项目中。
Dream并不是试图用AI更新AEC软件的唯一公司 — — 其他从事这项工作的创业企业包括Motif、Snaptrude和Arcol-Elder和Mathur声称，他们是唯一开发了即时设计整个建筑的建筑世界模型的公司。
Kimberly Tan是安德烈森·霍罗威茨(Andreessen Horowitz，称)的合伙人，虽然公司在一年多前首次投资Dream，但当启动模型只是一个想法时，她对他们的进步印象极深。 “我没有看到任何人采取技术方法，” “他们正在根据自己的模式做一切事情。”
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Andy Elder and Arjun Mathur were Stanford roommates who both studied computer science before spending a decade at Palantir. Elder led product design for a team of 800 people building software for U.S. defense and intelligence agencies, while Mathur served as a senior vice president in the company’s commercial practice. When the friends decided to build a startup early last year, there was no question they would do it together.
Despite having no direct background in architecture, Elder and Mathur were troubled by a growing problem: fewer buildings are constructed in the U.S. today than a few decades ago, as development costs have surged.
“We’ve gone from dial-up internet to super intelligent AI, but our ability to build the physical world around us has actually decreased,” Mathur told TechCrunch.
Believing AI can boost productivity across the architecture, engineering, and construction (AEC) industry, they set out to build Dream, a startup developing world models that automate building design and engineering. Dream has emerged from stealth with $17 million in seed funding led by Andreessen Horowitz, alongside other backers including SV Angel, XYZ, and Zigg Capital. The startup aims to replace cumbersome, outdated AEC tools like Autodesk’s AutoCAD and Revit.
If you sit next to an architect or a structural engineer, you’ll notice that they are still “drawing” every piece of their designs part-by-part on legacy software, Mathur said. “AI really plays no role in that equation.”
While a standard LLM like ChatGPT can generate an image of a building, the result won’t be structurally sound. Dream, by contrast, is grounded in real physics and construction standards. Just as LLMs learn from text, Dream’s world model is trained on more than 100 million square feet of building data gathered from permit archives across the United States.
“Our model actually understands what’s within those walls. What materials are used, what the beams are made of, and how much weight they can support,” Elder said.
The startup calls its approach “structural cognition” because its model generates structurally precise designs that account for internal building elements like plumbing and HVAC.
Dream has already begun selling its product to AEC professionals and developers, who use the model to conceptualize building designs and estimate overall costs, according to the founders. The platform is already deployed across 10 active projects in Texas and California.
While Dream isn’t the only company attempting to update AEC software with AI – other startups working on this include Motif, Snaptrude and Arcol—Elder and Mathur claim that they are the only ones that developed a built world model that instantly designs whole buildings.
Kimberly Tan, a partner at Andreessen Horowitz, said that while the firm first invested in Dream over a year ago, when the startup model was just an idea, she has been extremely impressed with their progress. “I have not seen anybody take the technical approach,” she said. “They’re doing everything based on their own models.”
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