David McJannet

FULL $NVDA CEO JENSEN HUANG COMMENT ON THE AI BUBBLE FROM THE EARNINGS CALL: There’s been a lot of talk about an AI bubble. From our vantage point, we see something very different. As a reminder, Nvidia is unlike any other accelerator. We excel at every phase of AI, from pre-training and post-training to inference. And with our two-decade investment in CUDA acceleration libraries, we are also exceptional at science and engineering simulations, computer graphics, structured data processing to classical machine learning. The world is undergoing three massive platform shifts at once, the first time since the dawn of Moore’s Law. Nvidia is uniquely addressing each of the three transformations. The first transition is from CPU general-purpose computing to GPU-accelerated computing. As Moore’s Law slows, the world has a massive investment in non-AI software, from data processing to science and engineering simulations, representing hundreds of billions of dollars in compute and cloud computing spend each year. Many of these applications, which ran once exclusively on CPUs, are now rapidly shifting to CUDA GPUs. Accelerated computing has reached a tipping point. Secondly, AI has also reached a tipping point and is transforming existing applications while enabling entirely new ones. For existing applications, generative AI is replacing classical machine learning in search ranking, recommender systems, ad targeting, click-through prediction, and content moderation, the very foundations of hyperscale infrastructure. Meta’s GM of foundation models for ad recommendations, trained on large-scale GPU clusters, exemplifies this shift. In Q2, Meta reported over a 5% increase in ad conversions on Instagram and a 3% gain on Facebook Feed, driven by generative-AI-based GM. Transitioning to generative AI represents substantial revenue gains for hyperscalers. Now a new wave is rising: AI systems capable of reasoning, planning, and using tools, from coding assistants like Cursor and Cloud Code to radiology tools like iDoc, legal assistants like Harvey, and AI chauffeurs like Tesla FSD and Waymo. These systems mark the next frontier of computing. The fastest-growing companies in the world today — OpenAI, Anthropic, xAI, Google, Cursor, LLM-focused startups like Lovable, Replit, Cognition AI, OpenEvidence, Abridge, Tesla — are pioneering agentic AI. So there are three massive platform shifts. The transition to accelerated computing is foundational and necessary, essential in a post-Moore’s Law era. The transition to generative AI is transformational and necessary, supercharging existing applications and business models. And the transition to agentic and physical AI will be revolutionary, giving rise to new applications, companies, products, and services. As you consider infrastructure investments, consider these three fundamental dynamics. Each will contribute to infrastructure growth in the coming years. Nvidia is chosen because our singular architecture enables all three transitions and does so for any form and modality of AI, across all industries, across every phase of AI, across all of the diverse computing needs in the cloud, and also from cloud to enterprise to robots. One architecture.

All the analysts forever writing about OpenAI vs Anthropic vs Google are missing the real story that already happened. 80% of startups pitching Andreessen Horowitz are running on Chinese open-source models. Not OpenAI. Not Anthropic. Chinese models like DeepSeek that cost 214x less per token. The math here breaks everything. DeepSeek trained its model for $5 million. OpenAI spent $500 million per six-month training cycle for GPT-5. That gap translates directly to API pricing where startups pay $0.14 per million tokens versus $30 for GPT-4. For a startup burning through 100 million tokens monthly, that’s $1,400 versus $300,000. The difference between 18 months of runway and 3 months. This tells you the real constraint in AI was never capability. Chinese models are matching GPT-4 on coding benchmarks while costing 2% as much. The constraint was always burn rate, and China solved it first by optimizing for efficiency instead of chasing AGI. The second-order effect gets interesting. When your infrastructure costs drop 98%, you can actually afford to fine-tune models for your specific use case. American startups paying OpenAI’s API rates are stuck with generic models. Chinese open-source users are building specialized variants. Silicon Valley thought the moat was model quality. Turns out the moat was cost structure, and they built it backwards. When a16z partner Anjney Midha says “it’s really China’s game right now” in open-source, he’s not talking about benchmarks. He’s talking about who controls the default foundation layer. Now look at where this goes. American AI labs are optimizing for AGI and superintelligence. Raising billions to chase the theoretical ceiling. China optimized for distribution and adoption. Making AI cheap enough to become infrastructure. All 16 top-ranked open-source models are Chinese. DeepSeek, Qwen, Yi. The models actually being deployed at scale. While OpenAI charges premium rates for exclusive access, Chinese labs are flooding the zone with free alternatives that work. The third-order cascade is what changes everything. Every startup that survives the next funding winter will have optimized around Chinese open-source as default infrastructure. Not as a China strategy. As a survival strategy. That 80% number at a16z only goes one direction. When you’re a seed-stage founder choosing between 18 months of runway or 3 months, economics beats nationalism every time. America is still competing to build the best model. China already won the race to build the one everyone uses.