Interview with an $NVDA employee on edge AI adoption, ecosystem dynamics, and the shift toward on-device compute ( $GOOGL, $AMZN ): - The expert identified two primary drivers behind enterprise decisions to move workloads to the edge rather than to the cloud. The first is cost, with cloud scalability becoming increasingly expensive at scale, pushing large customers toward on-prem edge deployments that are more cost-effective for certain workloads. The second is the nature of the use case itself, with real-time and latency-sensitive applications that require edge devices rather than cloud infrastructure. - The expert observes that the AI hardware ecosystem is increasingly organized around builders who broker connections between startups and large enterprise customers, with the growth of smaller AI companies directly driving broader infrastructure demand. This represents an industry shift away from focusing exclusively on large platform players toward the smaller, more specialized companies building niche AI solutions. - The expert notes that 60-70% of their enterprise customers already have internal teams dedicated to model optimization, showing how embedded this capability has become across the industry. Optimization is not seen as commoditized; the real competitive advantage lies with companies that own the model and handle optimization, since the two become deeply intertwined and harder to separate, creating genuine stickiness. - The expert also highlights a broader cultural shift in how AI infrastructure providers are approaching customer relationships, moving away from hardware exclusivity toward supporting customers regardless of which underlying chips they use. The expert draws a contrast with $GOOGL's culture during their time there, where quota and competition were the primary drivers. - The expert expects a significant shift in how AI inference is distributed over the next two to five years, with around 65-70% running on-device or on-prem and 30-35% remaining in the cloud. According to the expert, the cloud will increasingly be reserved for large, heavy model workloads, while the majority of inference moves to edge and embedded devices.