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At A/O, we recently published an in-depth analysis of the applications and opportunities of AI technologies in the built world, including 1300+ startups. We’ve highlighted five key takeaways from the report. You can read the full report F.
Startups are using a combination of AI technologies including robotics, IoT, natural language processing and generative models to solve a diverse set of pain points across the built world, from energy efficiency and climate risk to construction and real estate sales.
Over the past ten years, AI-enabled built world startups in Europe and North America have received a total of $18.6B in venture funding, of which close to half was in the last two years ($8.6B). And in both 2020 and 2022, venture deals in AI-enabled built world startups overtook FinTech AI funding, reaching over 600 deals globally in 2021 alone.
Over the past five years, London has consistently been the number one city by deal flow, ahead of San Francisco, New York and Paris, and the top European city for invested equity. In fact, more deals were done in London over the time period (2017-2022) than Paris, Berlin, Dublin and Tel Aviv combined. However, the Bay Area is the leader when it comes to invested capital, raising $4.6B over the five-year time period.
Our analysis identifies Climate Risk, ESG Reporting + Compliance and Energy Assets as the three fastest-growing applications of AI in the Built World. Other clear growth verticals include Architectural, Engineering and Construction (AEC) Software, Smart Cities, Smart Buildings and Real Estate Asset Inspection – areas where the majority of startups have a strong climate focus. Meanwhile, the largest deals are concentrated in the Built World’s most mature segments: Real Estate Transactions, Retail, Property + Tenant Management, and Construction.
Over the past five years, Climate Risk, ESG Reporting + Compliance and Energy Assets have emerged as the top three fastest growing verticals for AI application in the Built World.
Applications are primarily concentrated in AEC Software, with features including 3D model creation from a 2D image, custom material render generation, optimised floor plan layouts, and even the generation of a Building Information Model from simple descriptive text. However, the industry's highly heterogeneous and context-specific nature of building-level data, coupled with the lack of readily available data, poses a challenge to generative AI's deployment. Despite the potential of generative models, digitisation and automation will be crucial in the short term.
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