🤖 AI Summary
At the APEC CEO Summit in South Korea, Nvidia CEO Jensen Huang described AI as being in a “virtuous cycle”: improvements in models drive wider use, which generates profit and spurs capital investment (capex), which in turn funds better models and infrastructure. He framed this feedback loop as the engine behind the current surge in AI spending by Big Tech — companies like Meta, Amazon, Alphabet and Microsoft are collectively committing hundreds of billions to AI and datacenter buildouts — and pointed to profitable scaling as the reason manufacturers keep building more capacity. Nvidia’s own momentum (it recently topped $5 trillion market value) and a new Samsung deal to deploy 50,000 Nvidia GPUs illustrate that momentum concretely.
Huang argued this is the start of a 10-year build-out that will fundamentally recast the computing stack: AI workloads run on GPUs and “accelerated computing,” not traditional CPU-driven, hand-coded software, so chips, energy, infrastructure, systems software, models and applications all must be redesigned. The implication for the AI/ML community is massive opportunity and shifting priorities — more emphasis on GPU-optimized model architectures, energy-efficient infrastructure, software tooling for distributed training/inference, and migration strategies for legacy compute as industries retool to capture what Huang estimates could be a $100 trillion reshaping of global economic activity.
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