Inevitable

Startup Series: Reclaimed Lumber with Urban Machine


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Eric Law is the CEO and co-founder of Urban Machine

Urban Machine develops robotics and AI to reclaim lumber from construction and demolition for re-use. They claim that 37 million tons of wood goes into landfills annually, and much of that wood could be reused. Their robotic machines specialize in the necessary steps to prep wood for reconditioning. 

In 2022, Urban Machine successfully secured a seed funding round, spearheaded by Lower Carbon Capital and supported by GV (Google Ventures).

With a career rooted in the construction and contracting industry, Eric has extensive experience in large construction projects and has been involved in creating and selling software targeted at this sector. We start our conversation with an overview of the construction industry, highlighting the main players, their challenges, including considerations around emissions and sustainability. 

The discussion touches on challenges Urban Machine aims to address, the innovative technology they employ, and their aspirations for the future.

In this episode, we cover: 

  • [3:08] Overview of the construction industry and its challenges, including labor shortages and sustainability concerns
  • [5:52] Key stakeholders in construction
  • [13:41] Urban Machine's mission to reclaim lumber from construction and demolition for reuse
  • [17:91] The importance of deconstruction in reducing wood waste
  • [19:30] Urban Machine's tech and processes for preparing wood for reconditioning
  • [21:38] Benefits of using reclaimed lumber in construction projects
  • [23:58] Economic considerations of using reclaimed lumber and competing with virgin lumber prices
  • [26:19] Urban Machine's progress to-date, funding and future vision 
  • [30:00] Eric Law's motivation for starting the company and its positive reception from the industry

Episode recorded on Mar 14, 2024 (Published on Mar 28, 2024)

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*Editing and post-production work for this episode was provided by The Podcast Consultant

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