Framework
Zero to One Before Zero to One Hundred
The dream of AI capturing decades of tribal engineering knowledge is real — but it is a future state. You cannot skip the unsexy infrastructure work that makes it possible.
How it works
- 1First you fix the basics: messy data, scattered files, broken processes, no single source of truth.
- 2You cannot one-shot from nothing to a knowledge-capturing AI. The foundation has to exist before the intelligence is worth building.
- 3This is true in every category: the AI is only as good as the data and process hygiene underneath it.
Hear it on the podcast
The Physicist Building a Compiler for the Real World | Hugo Nordell, EncubeListen to the episodeMore from this episode
The Three KPIs That Actually Matter in HardwareMost hardware teams track the wrong numbers. Hugo's framework focuses on the three levers that actually compound into competitive advantage.Shift LeftSurface trade-offs and manufacturability problems early in the design process, when they are still cheap to fix. By the time you reach production, the cost of a change can be measured in months.The Hardware CompilerSoftware AI agents are powerful because they can self-correct — there is a right answer and a fast feedback loop. Hardware never had that. Encube is building the equivalent.Deterministic Core, LLM EdgesRun the same compute twice and get the same answer twice. Physical products demand reproducibility. Large language models, left unguarded, cannot guarantee it.The Conductor ModelWhen computation takes over the heavy lifting, the engineer stops being a button pusher and becomes an orchestrator — exploring many design paths in the time it used to take to explore one.
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