Nvidia's path to $10 trillion looks clearer as GPT-6 Astra runs inference on Blackwell

VanquishTrader @VanquishTraderOriginally posted 2026.10.03

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WHY $NVDA PATH TO $10 TRILLION IS GETTING EASIER TO SEE The reason Nvidia hit a new all time high this week is that several of the market’s biggest concerns started breaking in its favor at the same time. The most important was inference, where OpenAI confirmed GPT 6 Astra Ultrafast runs on Blackwell and its inference lead said OpenAI’s own models can optimize Nvidia hardware across “the full frontier of latency, throughput and cost.” That matters because low latency inference was one of the clearest areas where specialists like Cerebras were supposed to take share, while custom silicon from Google and others was expected to fragment the market. Instead, this week reinforced the idea that frontier models are increasingly optimizing around Nvidia’s stack, which means CUDA and Blackwell can actually get more valuable as the models themselves improve. At the same time, demand visibility keeps stretching further out, which is exactly what Nvidia needed for the market to stop treating this like a short hardware cycle. Anthropic’s contracted value on Nvidia infrastructure has reportedly passed $180 billion through 2028, OpenAI continues raising enormous amounts of capital, and Micron is now saying memory remains tight through 2028 because AI system demand keeps outrunning supply whichstarts changing the way investors think about Nvidia because GPUs are not simply one time training hardware anymore. They are being reused across training, inference and reinforcement learning while always on agents make inference demand much more persistent, so the earnings stream increasingly looks like a durable platform rather than a boom and bust semiconductor cycle. The macro backdrop then removed one of biggest reasons investors had been unwilling to pay a higher multiple. Fed hike odds collapsed after softer inflation and jobs data while long term yields moved off the highs, which helps Nvidia twice because a lower discount rate supports the valuation directly and cheaper capital makes the data centers its customers are building easier to finance. There are still real risks because memory costs can pressure gross margins, Google is pushing TPUs aggressively and AMD is expanding deeper into the model and software layer, but those risks now sit against a business where each new phase of AI keeps widening Nvidia’s addressable market instead of replacing the last one. Training created the first leg, inference is becoming the recurring leg, and Physical AI can become the next. That is why I think the path toward a $10 trillion Nvidia is becoming easier to underwrite even though it obviously will not happen in a straight line. Earlier this year the stock traded at a discount to what its growth and strategic position arguably deserved because the market was still assuming inference would fragment and earnings would eventually normalize like a traditional chip cycle so if Nvidia can keep proving that its hardware is productive for longer, that its software moat compounds as frontier labs optimize directly for CUDA, and that contracted demand remains visible several years out, then the market can justify valuing a much larger stream of future earnings rather than just the next few quarters. At that point $10 trillion becomes less about paying an outrageous multiple and more about Nvidia growing into a much larger earnings base while the market gradually stops valuing it like a cyclical chip company.