Startup Project sits down with Sviat, CEO of Bright Machines, to unpack how the company is using software-first robotics to manufacture complex electronics closer to where they’re deployed. The conversation focuses on why AI infrastructure is a strategic category, how Bright Machines differs from traditional contract manufacturing, and what onshoring really means for speed, quality, and security. Key Topics: In this episode, Sviat explains that Bright Machines is focused on AI infrastructure, specifically the electronics that go inside modern data centers, including compute nodes, storage, and racks.He traces the company’s thesis back to a broader idea: use software and robotics to manufacture electronics anywhere, then narrow that focus to the data center market as demand became clearer.The discussion breaks down the market stack, from chip designers like NVIDIA and AMD, to ODMs, OEMs, hyperscalers, and contract manufacturers.Sviat shares why data center hardware became the right bet before ChatGPT accelerated the market: the products are expensive, strategically important, and driven by quality and throughput more than labor cost alone.The show compares traditional assembly lines with Bright Machines’ approach, which uses more robotics, sensors, cameras, traceability, and humans in the loop where automation does not make sense.Sviat explains how Bright Machines starts with design, using Bright Designer to simulate and improve manufacturability before lines are built, which helps reduce bottlenecks and improve automation over time.He says the company’s main differentiator is its software platform, which orchestrates the line, powers smart skills for navigation and inspection, collects data, and feeds insights back into design.The conversation covers line flexibility, including how much can be reused when switching between CPU, GPU, or different accelerator-based server designs, and when end-of-arm tooling must change.Sviat says Bright Machines is growing rapidly, expects more than 3x growth this year, and can produce high volumes from a small number of sites because of robotics efficiency.The episode closes on the broader case for onshoring AI infrastructure manufacturing in the US: security, time to market, quality, and a labor shortage that makes robotics necessary. Timestamps:06:39 - The market stack: chip designers, ODMs, OEMs, hyperscalers, and CMs 09:07 - Why Foxconn, Jabil, and similar contract manufacturers matter 10:04 - Why large factories still rely on massive manual labor 12:20 - Why data centers are different from cheap consumer electronics 13:49 - Security, strategic sectors, and why AI infrastructure belongs onshore 16:26 - The first Bright Machines product: CPU compute servers for a hyperscaler 17:58 - How the line works: modular stations, yields, and automation levels 19:26 - Bright Designer and design-for-manufacturing feedback loops 21:20 - Robots, sensors, traceability, and humans in the loop 22:19 - Why time to market matters as much as cost 23:31 - Yield and throughput: 98% line-level yields and up to 2x throughput 25:25 - The Bright Robotic Cell and how the assembly line is structured 27:35 - Reusability across products and when tooling changes are needed 30:31 - Manufacturing as a service, not repair or field service 31:24 - Growth, gigawatt-scale capacity, and output from a single site 33:00 - Why current hyperscaler capex is not expected to slow near term 34:45 - The bottlenecks before deployment: chips, components, power, permits 36:54 - Bright Machines’ three pillars: platform, data layer, and Bright Designer 39:15 - Why humanoid robotics is exciting but not ready for industrial use 41:16 - Where LLMs and newer AI tools can help the robotics workflow 43:57 - The overlooked advantages of onshoring manufacturing in the US 45:59 - What Bright Machines could build next: more complex electronics and future AI devices
