This episode explores why hardware engineering lags behind software in tooling and observability, and how Nominal's data platform bridges that gap by managing the hardware data supply chain. It highlights the unique challenges of high-frequency time-series sensor data, the shift towards iterative hardware development, and the potential for AI to transform hardware engineering in the future.
Summarized by Podsumo
Hardware engineering suffers from slow feedback loops and poor tooling compared to software, with data often transferred via hard drives.
Nominal addresses this by managing the hardware data supply chain from sensor ingestion to real-time monitoring and post-test analysis.
Hardware data is treated as 'pets' (every data point is critical), contrasting with software's 'cattle' approach (aggregating metrics).
AI agents are transforming software development but have not yet made the same leap in hardware due to physical constraints and safety risks.
Nominal is exploring AI integration, including a workbook agent for natural language queries, to reduce friction in hardware data analysis.
"“These startups are trying to go even faster than SpaceX. They're trying to do more with less than ever before.” — Jason Hawk"
"“For our customers, everything is a pet. They have one or two aircraft that the entire company is oriented around.” — Jason Hawk"
"“If you launch a satellite and it fails, that's months of timeline and tens of millions of dollars. You're willing to double check things a lot more.” — Jason Hawk"