Definition
eBOM
Engineering Bill of Materials — the full, structured list of every part in a product and how they relate. The eBOM is the source of truth for what actually gets manufactured.
From the episode “The Physicist Building a Compiler for the Real World | Hugo Nordell, Encube”Hear it on the podcast1 episode
Learn the language of AI-native founders
Every week: the new terms, tools, and workflows founders are using, straight from the podcast.
Related terms in Product & Engineering
Digital twinA verified, licensable likeness of a real person, usually face and optionally voice, that productions can use in AI-generated content without a live performance.DOMDocument Object Model, the live structure of a webpage that Cora manipulates without touching your code.DownloadsBrett Hurt's term for insights that arrive during meditation.DPIA (Data Protection Impact Assessment)A structured risk review required before running processing activities likely to result in high risk to individuals' rights and freedoms.Edge DeviceAny computing hardware that processes data locally without sending it to a remote server or cloud. Examples include smartphones, drones, and the Key Boy. Critical for latency-sensitive and privacy-sensitive applications.ELT PipelineExtract, Load, Transform. The process of moving raw data into a warehouse before transforming it — one of the four highest-value use cases for data agents.Engineer vs. CoderEngineers are system architects — they decide what to build, how components fit together, what problems the architecture needs to solve. Coders implement those specifications. The distinction matters because coders can increasingly be replaced by AI that executes specifications quickly and cheaply; architects cannot. Founders who staff with coders instead of engineers get fast execution without coherent architecture. The correct hire for a technical team is engineering judgment, not implementation capacity.Enterprise Data Technical DebtThe accumulated cost of maintaining, working around, and compensating for outdated, disorganized, or incompatible data infrastructure. Analogous to software technical debt but measured at the data layer rather than the code layer. Includes the ongoing cost of maintaining legacy mainframe systems that cannot be retired without major migration, the cost of manual data cleaning and reconciliation work performed by human analysts, and the cost of enterprise AI projects that underperform because data quality cannot support the intended use case. The Wall Street Journal cites this as a $1.52 trillion annual problem in the US alone. David Carmel uses the metaphor of Victorian era plumbing: the pipes are old, they work after a fashion, but they are fundamentally incompatible with the modern infrastructure being built around them.