Due Diligence

“Oftentimes, if you reason about things from first principles, what’s working today incredibly well — if you could reason about it from first principles and ask yourself on what foundation that first principle is built and how that would change over time — it allows you to hopefully see around corners.” – Jensen Huang – CEO Nvidia

Jensen Huang’s quote was delivered in the context of an in-depth dialogue with institutional investors on the trajectory of Nvidia, the evolution of artificial intelligence, and strategies for anticipating and shaping the technological future.

Context of the Quote

The quote was made during an interview at a Citadel Securities event in October 2025, hosted by Konstantine Buhler, a partner at Sequoia Capital. The dialogue’s audience consisted of leading institutional investors, all seeking avenues for sustainable advantage or ‘edge’. The conversation explored the founding moments of Nvidia in the early 1990s, through the reinvention of the graphics processing unit (GPU), the creation of new computing markets, and the subsequent rise of Nvidia as the platform underpinning the global AI boom. The question of how to ‘see around corners’ — to anticipate technology and industry shifts before they crystallise for others — was at the core of the discussion. Huang’s answer, invoking first-principles reasoning, linked Nvidia’s success to its ability to continually revisit and challenge foundational assumptions, and to methodically project how they will be redefined by progress in science and technology.

Jensen Huang: Profile and Approach

Jensen Huang, born in Tainan, Taiwan in 1963, immigrated to the United States as a child, experiencing the formative challenges of cultural dislocation, financial hardship, and adversity. He obtained his undergraduate degree in electrical engineering from Oregon State University and a master’s from Stanford University. After working at AMD and LSI Logic, he co-founded Nvidia in 1993 at 30, reportedly at a Denny’s restaurant. From the outset, the company faced daunting odds — neither established market nor assured funding, and frequent existential risk in the initial years.

Huang is distinguished not only by technical fluency — he is deeply involved in hardware and software architecture — but also by an ability to translate complexity for diverse audiences. He eschews corporate formality in favour of trademark leather jackets and a focus on product. His leadership style is marked by humility, a willingness to bet on emerging ideas, and what he describes as “urgent innovation” born of early near-failure. This disposition has been integral to Nvidia’s progress, especially as the company repeatedly “invented markets” and defined entirely new categories, such as accelerated computing and AI infrastructure.

By 2024, Nvidia became the world’s most valuable public company, with its GPUs foundational to gaming, scientific computing, and, critically, the rise of AI. Huang’s awards — from the IEEE Founder’s Medal to listing among Time Magazine’s 100 most influential — underscore his reputation as a technologist and strategic thinker. He is widely recognised for being able to establish technical direction well before it becomes market consensus, an approach reflected in the quote.

First-Principles Thinking: Theoretical Foundations

Huang’s endorsement of “first principles” echoes a method of problem-solving and innovation associated with thinkers as diverse as Aristotle, Isaac Newton, and, in the modern era, entrepreneurs and strategists such as Elon Musk. The essence of first-principles thinking is to break down complex systems to their most fundamental truths — concepts that cannot be deduced from anything simpler — and to reason forward from those axioms, unconstrained by traditional assumptions, analogies, or received wisdom.

• Aristotle was the first to coin the term “first principles”, distinguishing knowledge derived from irreducible foundational truths from knowledge obtained through analogy or precedent.
• René Descartes advocated for systematic doubt and logical rebuilding of knowledge from foundational elements.
• Richard Feynman, the physicist, was famous for urging students to “understand from first principles”, encouraging deep understanding and avoidance of rote memorisation or mere pattern recognition.
• Elon Musk is often cited as a contemporary example, applying first-principles thinking to industries as varied as automotive (Tesla), space (SpaceX), and energy. Musk has described the technique as “boiling things down to the most fundamental truths and then reasoning up from there,” directly influencing not just product architectures but also cost models and operational methods.

Application in Technology and AI

First-principles thinking is particularly powerful in periods of technological transition:

• In computing, first principles were invoked by Carver Mead and Lynn Conway, who reimagined the semiconductor industry in the 1970s by establishing the foundational laws for microchip design, known as Mead-Conway methodology. This approach was cited by Huang as influential for predicting the physical limitations of transistor miniaturisation and motivating Nvidia’s focus on accelerated computing.
• Clayton Christensen, cited by Huang as an influence, introduced the idea of disruptive innovation, arguing that market leaders must question incumbent logic and anticipate non-linear shifts in technology. His books on disruption and innovation strategy have shaped how leaders approach structural shifts and avoid the “innovator’s dilemma”.
• The leap from von Neumann architectures to parallel, heterogenous, and ultimately AI-accelerated computing frameworks — as pioneered by Nvidia’s CUDA platform and deep learning libraries — was possible because leaders at Nvidia systematically revisited underlying assumptions about how computation should be structured for new workloads, rather than simply iterating on the status quo.
• The AI revolution itself was catalysed by the “deep learning” paradigm, championed by Geoffrey Hinton, Yann LeCun, and Andrew Ng. Each demonstrated that previous architectures, which had reached plateaus, could be superseded by entirely new approaches, provided there was willingness to reinterpret the problem from mathematical and computational fundamentals.

Backstory of the Leading Theorists

The ecosystem that enabled Nvidia’s transformation is shaped by a series of foundational theorists:

• Mead and Conway: Their 1979 textbook and methodologies codified the “first-principles” approach in chip design, allowing for the explosive growth of Silicon Valley’s fabless innovation model.
• Gordon Moore: Moore’s Law, while originally an empirical observation, inspired decades of innovation, but its eventual slow-down prompted leaders such as Huang to look for new “first principles” to govern progress, beyond mere transistor scaling.
• Clayton Christensen: His disruption theory is foundational in understanding why entire industries fail to see the next shift — and how those who challenge orthodoxy from first principles are able to “see around corners”.
• Geoffrey Hinton, Yann LeCun, Andrew Ng: These pioneers directly enabled the deep learning revolution by returning to first principles on how learning — both human and artificial — could function at scale. Their work with neural networks, widely doubted after earlier “AI winters”, was vindicated with landmark results like AlexNet (2012), enabled by Nvidia GPUs.

Implications

Jensen Huang’s quote is neither idle philosophy nor abstract advice — it is a methodology proven repeatedly by his own journey and by the history of technology. It is a call to scrutinise assumptions, break complex structures to their most elemental truths, and reconstruct strategy consciously from the bedrock of what is not likely to change, but also to ask: on what foundation do these principles rest, and how will these foundations themselves evolve.

Organisations and individuals who internalise this approach are equipped not only to compete in current markets, but to invent new ones — to anticipate and shape the next paradigm, rather than reacting to it.