In$NVIDIA (NVDA.US)$ At the annual GTC conference, founder and CEO Jensen Huang announced new products and partnerships, unveiling a revenue opportunity of up to $1 trillion by 2027, which sparked positive reactions from analysts. Wedbush described the $1 trillion order backlog as "staggering."
The analyst team led by Dan Ives stated that Jensen Huang provided an in-depth exploration of AI infrastructure, quantum computing, telecommunications, and physical AI, solidifying NVIDIA’s position at the forefront of AI demand trends for 2026 and beyond. The analysts added that this serves as a much-needed confidence booster for technology investors navigating complex markets. Jensen Huang made it clear that despite market noise, the "AI revolution" is accelerating rather than slowing down.
Ives and his team noted: "At the 2026 GTC conference, Jensen Huang significantly raised the bar, announcing that NVIDIA now expects revenue opportunities exceeding $1 trillion by 2027 driven by the Blackwell/Rubin platform. This builds on the $500 billion announced at the GTC Washington conference last October, with robust demand coming from all directions... As agent-based AI and physical AI applications drive computational needs far beyond expectations from a year ago, enterprises, sovereign nations, and AI-native companies are deepening their investments in NVIDIA's infrastructure."
Analysts pointed out that inference has become the dominant demand driver. Compared to Hopper, GB200 NVL72 delivers up to 50 times higher performance per watt and reduces cost per token by 35 times, making it the preferred architecture for enterprises scaling agent-based AI workloads.
The analysts added that NVIDIA’s ambitions extend far beyond chips. The company officially launched NemoClaw, an open-source enterprise-grade AI agent platform designed to capture a 100-fold increase in inference demand as agent loops become the enterprise standard.
Regarding physical AI, analysts believe that Omniverse Blueprint’s physics engine supports factory-scale digital twins and robotic simulations, further expanding a vertical sector with a potential total market size of hundreds of billions of dollars over the next decade.
Ives and his team stated: "We estimate that for every $1 spent on NVIDIA chips, there is a multiplier effect of $8 to $10 across the entire ecosystem. Hyperscale data centers, software, data center construction, cybersecurity, and power/energy sectors will all benefit from $3 to $4 trillion 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 kicked off with Jensen Huang’s gold standard, and he did not disappoint."
JPMorgan maintained its 'Overweight' rating and $265 price target for NVIDIA stock.
The analyst team led by Harlan Sur stated: "In short, while market debates have shifted to the duration of the AI spending cycle, we believe NVIDIA’s vertically integrated platform (now encompassing seven types of chips, five rack systems, and the software stack that ties them together) is difficult to replicate. The combination of accelerating inference demand, structural TAM expansion through traditional workload acceleration, and an expanding customer base supports a cycle more durable than current market expectations."
Analysts noted that NVIDIA’s management increased visibility into demand for Blackwell and Vera Rubin shipments/purchase orders through 2027 to over $1 trillion, compared to the $500 billion figure announced at the GTC Washington, D.C., event last October for 2026. According to JPMorgan estimates, this implies upside potential of at least $50 billion to $70 billion relative to consensus expectations for 2026-27 data center revenues, with additional orders/backlog for 2027 likely to increase in the next 6 to 9 months."
Moreover, analysts believe that a significant but underappreciated part of the keynote speech pertains to accelerating traditional enterprise workloads through the CUDA-X libraries. CUDA-X is a collection of GPU-accelerated libraries, microservices, and tools built on NVIDIA's CUDA parallel computing platform.
Soul and his team stated: "The integration of NVIDIA's Groq 3 Language Processing Unit with Vera Rubin represents the most architecturally significant product launch—a split inference architecture pairing Rubin GPUs (high throughput) with Groq LPUs (low-latency decoding), enabling NVIDIA to effectively serve the low-latency inference market (where ASICs have traditionally held an advantage)."
Editor/Lambor