$NVIDIA(NVDA)$ didn’t sneak up on the world -- the world simply didn’t realize what it had built. It wasn't just chips. It wasn’t just GPUs. It was a new substrate for value creation. A new rail system for intelligence itself.
Because we’re no longer living in an internet-first world. We’re living in an intelligence-first one. Where product design, logistics, diagnostics, creative tools, drug pipelines, legal services, national defense -- all of it -- are shifting from deterministic software to probabilistic intelligence. And that shift requires compute. Unimaginable amounts of it. Not once, but perpetually. Not centrally, but everywhere.
Blackwell isn’t a chip. It’s a control layer. It’s the thing hyperscalers and governments alike are racing to secure not because they want to run proofs of concept -- but because they know that without it, they don’t get to participate in the next economy. A single Blackwell rack can cost $3M -- and they’re backordered. This isn’t a bubble that all the bears try to spread. This is a global bidding war for the new oil.
And yet, the noise persists.
“Capex is peaking.”
“Big Tech is building their own silicon.”
“China has caught up.”
“CUDA is replaceable.”
“AI demand isn’t monetizing fast enough.”
But here’s the reality: none of it holds up under scrutiny.
$Amazon.com(AMZN)$ , $Alphabet(GOOGL)$ , $Meta Platforms, Inc.(META)$ & $Apple(AAPL)$ may be designing chips. But they’re still training their models on NVIDIA infrastructure. They still benchmark performance with CUDA. They still build in a development environment NVIDIA owns end to end. Owning inference doesn’t matter if you can’t get your model production-ready -- and CUDA is the only ecosystem that makes that seamless. It’s not just a moat. It’s gravity.
China’s Huawei may have engineered a chip that mimics the H20 -- but let’s not lose the plot. H20 was designed under export restriction. It’s decades behind. Matching that performance is like building a TSLA that performs like a 2015 Prius. And no -- DeepSeek didn’t train with less compute because China suddenly became efficient. It trained with less because China’s domestic supply is starving. The famine isn’t metaphorical. It’s real. And it’s why sovereigns are now stockpiling NVIDIA systems like strategic reserves.
As for capex? Of course hyperscaler spend will normalize. But that’s like saying road construction slows down once the highways are built. Inference is what comes next. And inference is exponential. Training happens in stages. Inference happens every second of every day, across every app, every device, every industry. And guess who’s built the only full-stack system that can scale that?
The thing people miss is this: NVIDIA doesn’t just sell to cloud providers. It sells to every company trying to escape dependence on them. They all need compute. And not just hardware. They need software, firmware, drivers, simulators, developer tools, vertical models, reference architectures. They need a system. NVIDIA is the only company that offers one.
So when people ask: Is NVIDIA the next $10 trillion company? -- they’re asking the wrong question.
They’re still thinking in quarters. Still thinking in cycles. Still asking if the numbers are sustainable, if margins will hold, if unit volumes will surprise to the upside.
But this isn’t a product story. It’s a platform shift. It’s a new industrial base.
Because NVIDIA isn’t just a chip company anymore -- it’s the compute layer of the modern world. What Amazon was to commerce. What Google was to knowledge. What Microsoft was to productivity. NVIDIA is becoming to intelligence.
And if the 21st-century economy runs on AI, then NVIDIA doesn’t just supply the picks and shovels.
It owns the mine.
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