Everyone is talking about a water crisis brought on by AI use. But think of this: the data centers powering the AI economy are not built from code. They require concrete, steel, cranes, and the coordination of hundreds of subcontractors across millions of square feet of new floor space.<\/p>\n\n\n\n
And right now, the construction industry is struggling to keep up.<\/p>\n\n\n\n
Capital expenditure from the 14 largest publicly owned data center operators globally is projected to approach $750 billion<\/a> in 2026, up from under $450 billion the year prior. The Stargate Project alone, a multi-year, $500 billion plan<\/a> to deliver up to 10 gigawatts of AI-ready power, was formed as a joint venture between OpenAI, SoftBank, Oracle, and MGXis and spans multiple U.S. states.<\/p>\n\n\n\n Meanwhile, the median cost of building a data center hit $445 per square foot this year, up 7.4%<\/a> from 2025, with average costs skewed far higher by hyperscale projects. The pipeline is enormous, and yet the pressure on builders to deliver is greater still.<\/p>\n\n\n\n Erez Dror has seen this shift from both sides. A structural engineer and former construction superintendent who spent over a decade on job sites in Israel, he co-founded workforce intelligence platform, Genda, which last year was acquired by Buildots after a $5.5 million Seed round. He recently stepped into the new role of VP of General Contractors & Genda at Buildots, which to date has raised $166 million, and featured on Forbes’ “Billion Dollar Startups”<\/a> list.<\/p>\n\n\n\n After the acquisition, the joint entity is now positioning itself as the operational backbone for exactly the kind of complex, fast-moving builds that the AI infrastructure boom demands.<\/p>\n\n\n\n \u201cA product executive who worked on the biggest project Genda was on, a $600 million project, took them four years to build,\u201d Dror told me. \u201cHe moved to build one of the biggest data centers in the U.S., which was $6 billion \u2014 10x the scale \u2014 and they built it in three years. A year less, and 10x times the scale.\u201d<\/p>\n\n\n\n The compression reflects a new standard being set by hyperscalers who come from a software-first culture and expect physical construction to behave accordingly. \u201cA person who works for Google and is used to building software that doesn\u2019t break expects to get a building that doesn\u2019t break at the same quality,\u201d he said. \u201cThey\u2019re setting a new standard, which I believe will eventually trickle down to everything.\u201d<\/p>\n\n\n\n The challenge is that construction remains one of the most fragmented, data-poor industries in the global economy. Unlike a tech organization, where a single executive decision can transform operations overnight, construction is built around individual projects with its own lead, subcontractors, or even its own tolerance for disruption. Change management, Dror argues, is \u201cjust a different beast.\u201d<\/p>\n\n\n\n That fragmentation is precisely what Buildots is trying to solve. The platform ingests two data streams: weekly 360-degree camera footage from job sites and the project\u2019s 3D building model, to use computer vision to identify what has been built versus what was planned.<\/p>\n\n\n\n