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Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
In this episode, Pari Singh, founder and CEO of Flow Engineering, reveals how the traditional waterfall approach to hardware engineering is fundamentally broken and why a paradigm shift to agile, iterative methodologies is essential. Singh shares his journey from building rocket engines to creating software that reimagines how complex hardware is designed across industries. He discusses the generational change happening from old-school companies like Boeing to new-wave companies like SpaceX, the competitive threat from China's ultra-hardcore manufacturing approach, and why complexity demands bottom-up rather than top-down engineering processes. (01:00)
Main theme: The urgent need for hardware engineering to evolve from waterfall to agile methodologies to handle increasing system complexity and compete globally.
Pari Singh is the founder and CEO of Flow Engineering, a company revolutionizing how complex hardware is designed. Originally a mechanical engineer who worked at companies like BAE Systems and BP, Singh started his career building rocket engines before pivoting to software. His company Flow has evolved from being the world's fastest design consultancy for hybrid rocket engines (capable of doing in 2.5 hours what traditionally took 12 weeks) to creating integrated platforms that enable agile hardware development across industries including aerospace, nuclear, robotics, and automotive.
Traditional hardware engineering relies on waterfall methodologies where teams spend months writing requirements and creating Gantt charts before building anything. Singh argues this approach fails when designing complex systems because "the unknown unknowns are way bigger than the list of known unknowns." (08:19) Instead of trying to predict every requirement upfront, successful companies like SpaceX take a bottom-up approach, asking "what can we produce in the next three months?" and iterating rapidly. This mirrors how the Apollo program succeeded through iterative missions (Mercury, Gemini, Apollo) rather than attempting to design everything perfectly from the start. (10:41)
Singh emphasizes that breakthrough innovation requires accepting failure as inevitable and valuable. The key differentiator between traditional companies and successful new-wave hardware companies is their relationship with failure. While traditional companies suffer from "analysis paralysis" trying to derisk everything before building, successful companies understand that "learning can only happen iteratively by doing stuff, accepting probability of failure, and being okay with that." (12:42) This philosophy enabled SpaceX to achieve rapid progress through multiple test flights rather than spending years in theoretical design phases.
While embracing failure for learning, Singh identifies a critical sliding scale between "let's just test it and it will break" versus "this can't go wrong." (20:27) The key is knowing when to lean toward rapid iteration (early development phases) versus safety and reliability (when humans are in the loop). This balance gives the US a potential competitive advantage over China, where Singh observes they "haven't quite worked out what the right balance between these two elements is," citing examples of Chinese rockets crashing into populated areas. (20:35)
The traditional model of functional silos (mechanical engineers, systems engineers, project managers working separately) breaks down with complex systems. Singh advocates for "responsible engineers" - a concept popular in El Segundo aerospace companies - where individuals combine mechanical engineering, systems engineering, project management, and manufacturing expertise. (30:04) These REs "take ultimate responsibility for the requirements and how we deliver against it," enabling faster decision-making and better integration across disciplines. This approach is essential because modern hardware is increasingly software-defined, requiring deep cross-functional understanding.
Singh learned that even revolutionary technology needs to be packaged in familiar terms for adoption. When Flow's advanced integrated modeling platform failed to gain traction, they reframed it as "requirements management plus plus." (43:37) As Singh explains, people are "bad at new things" - the first cars were called "horseless carriages" because people understood carriages but not automobiles. (40:15) By solving an immediate problem (requirements management) while delivering transformational capabilities, they achieved rapid adoption where revolutionary messaging had failed.