Wall Street Reassesses AI Outlook: NVIDIA (( NVDA.US )) Expected to Reach a Trillion-Dollar Valuation, Raising the Growth Ceiling

At NVIDIA’s (NVDA.US) annual GTC conference, the founder and CEO Huang Renxun unveiled new products and partnership announcements, and disclosed a revenue opportunity of as much as $1 trillion by 2027, which sparked a positive reaction from analysts. Wedbush said the $1 trillion order backlog is “astonishing.”

A team of analysts led by Dan Ives said Huang Renxun went deep into AI infrastructure, quantum computing, telecoms, and physical AI, strengthening NVIDIA’s position at the top of the AI demand curve in 2026 and beyond. The analysts added that this serves as a much-needed confidence boost for tech investors navigating a complex market. Huang Renxun said clearly that despite persistent market noise, the “AI revolution” is accelerating, not slowing down.

Ives and his team pointed out: “At the 2026 GTC conference, Huang Renxun raised the bar significantly, announcing that NVIDIA now expects that by 2027, the Blackwell/Rubin platform will deliver more than a $1 trillion revenue opportunity. This builds on the $500 billion base announced at the GTC Washington meeting in October last year, with demand coming from all directions… As agentic AI and physical AI application-driven compute needs substantially exceed expectations from a year ago, enterprises, sovereign nations, and AI-native companies are all deepening their investment in NVIDIA’s infrastructure.”

Analysts noted that inference has become the dominant demand driver. Compared with Hopper, the GB200NVL72 offers up to 50x better performance per watt, and reduces the cost per token by 35x, making it the preferred architecture for enterprises to scale agentic AI workloads.

The analysts added that NVIDIA’s ambition goes far beyond chips. The company officially launched NemoClaw, an open-source, enterprise-grade AI agent platform designed to capture 100x growth in inference demand as agent loops become the enterprise standard.

On the front of physical AI, analysts believe that the Omniverse Blueprint physics engine supports factory-scale digital twins and robotics simulation, further expanding a vertical market with a potential total addressable market of up to several tens of billions of dollars over the next decade.

Ives and his team said: “We estimate that for every $1 spent on NVIDIA chips, there will be a $8 to $10 multiplier effect across the broader ecosystem. Ultra-hyperscale data centers, software, data center construction, cybersecurity, and power/energy will all benefit from $30 to $40 billion in AI capital expenditures over the next three years. NVIDIA’s chips remain at the core of this fourth industrial revolution. Overall, today’s GTC conference got things started with Huang Renxun’s gold standard, and he didn’t disappoint.”

JPMorgan maintained a “buy” rating on NVIDIA’s stock and a $265 price target.

A team of analysts led by Harlan Suhr said: “In short, while market debate has shifted to the duration of the AI spending cycle, we believe NVIDIA’s vertically integrated platform (which now covers seven chip types, five rack systems, and a software stack that integrates them together) is difficult to replicate. The combination of accelerating inference demand, structural expansion of the addressable market driven by acceleration through traditional workloads, and an expanding customer base supports a cycle that is more durable than what the market currently expects.”

The analysts noted that NVIDIA management will increase demand visibility for shipments/purchase orders of Blackwell and Vera Rubin by 2027 to more than $1 trillion, versus $500 billion for 2026 announced at the GTC conference in Washington, D.C. last October. According to JPMorgan’s estimates, this implies at least $50 to $70 billion of upside compared with the market’s general expectations for 2026-27 data center revenue, and that additional orders/backlog for 2027 could increase within the next 6 to 9 months.

In addition, analysts believe that a meaningful but undervalued part of the keynote address is about accelerating traditional enterprise workloads through the CUDA-X library. CUDA-X is a collection of GPU-accelerated libraries, microservices, and tools built on NVIDIA’s CUDA parallel computing platform.

Suhr and his team said: “The Groq3 language processing units integrated with Vera Rubin are the most important product announcements architecturally—it’s a split inference architecture that pairs Rubin GPUs (high throughput) with Groq LPU (low-latency decoding), enabling NVIDIA to effectively serve the low-latency inference market (where ASICs have traditionally had an advantage).”

As of the time of publication, ahead of Tuesday’s trading, NVIDIA’s stock price was essentially flat.