Why Is The Market Not Buying Nvidia Despite Its USD 1 Trillion Potential Revenue?
πππAt GTC 2026, NVIDIA $NVIDIA(NVDA)$ CEO Jensen Huang has officially raised the stakes, forecasting a staggering USD 1 Trillion in revenue from Blackwell and Rubin chips through 2027. This is a massive leap from last year's USD 500 billion estimate, positioning NVIDIA not just as a chipmaker but as the "Digital Guardian" of a full stack AI ecosystem.
The Trillion Dollar Disconnect
Despite the Super Bowl of AI (GTC 2026) delivering headline grabbing numbers, Nvidia's share price has remained largely range bound between USD 180 and USD 190.
Here is why the market is currently "looking the gift horse in the mouth":
"Sell the News" Syndrome: After a historic rally to a USD 5 Trillion valuation in late 2025, many investors are using these massive projections as a cue to take profits rather than double down.
The "Inference" Battle: Jensen Huang spent much of his GTC keynote (mentioning it 36 times) trying to prove Nvidia can dominate the Inference phase of AI. However the market remains cautious about rising competition from custom chips at Google and Amazon.
What is Inference?
Think of AI in 2 stages:
Training - The Learning Phase: This is like a student studying a library of books for years. It requires massive, general purpose power to find patterns. Nvidia's Blackwell GPUs are currently kings of this phase.
Inference - The "Doing" Phase: This is the moment you ask ChatGPT a question or a self driving car spots a stop sign. The model is "inferring" an answer based on its previous training. It does not need to learn anymore. It just needs to respond instantly and cheaply.
The Shift: As AI moves from research labs to billions of users, inference is becoming the largest portion of all AI compute.
The Custom Killers: Google and Amazon's Custom Chips
The Big Tech giants are tired of paying the "NVIDIA Tax" - the massive profit margin that NVIDIA charges for its chips. To fight back, they have built their own custom ASICs or Application Specific Integrated Chips designed specifically for their own clouds.
Google's TPU v7 Ironwood:
Google $Alphabet(GOOG)$
Google's TPUs are reportedly 3x to 4x cheaper to operate than using Nvidia's H100.
Anthropic has just placed a USD 11 billion order for Google's custom chips, a direct blow to Nvidia's supremacy.
Amazon's Inferential and Trainium:
$Amazon.com(AMZN)$ Inferentia2 and Trainium2 are built by its Annapurna Labs.
AWS claims 30 to 40 % better price to performance than using Nvidia's GPUs for specific workloads.
Macro Headwinds
The "War Premium" from the Iran war and reduced expectations for interest rate cuts are weighing on high multiples tech stocks, making it hard for even a USD 1 Trillion forecast to trigger a breakout.
Validation vs Surprise
Many analysts believe the USD 1 Trillion figure simply validates existing expectations rather than providing a fresh "upside surprise".
Nvidia's Full Stack Financials (FY 2026)
NVIDIA is already operating at a level that would make a sovereign nation jealous:
Record Revenue: USD 215.9 billion for fiscal 2026, up 65% YoY.
Data Centre Dominance: Contributed USD 62.3 billion in the final quarter alone, accounting for 91% of total revenue.
Profit Machine: Gross margins are holding steady at a jaw dropping 75%.
Concluding Thoughts
NVIDIA is currently the "High Priced Landlord" of the AI world but its biggest tenants - Google and Amazon have just started building their own apartment complexes next door.
There is a touch of dark humour in the market's hesitation. Nvidia is forecasting USD 1 Trillion in revenue, yet the stock is flat because Big Tech giants are slowly learning how to breathe without Nvidia's oxygen.
Google's Ironwood and Amazon's Trainium are the specialised "digital guardians". They don't need to do everything. They just need to do AI inference cheaper than Nvidia.
It is a homecoming to the reality of 2026: Scarcity created Nvidia's dominance but efficiency will determine its legacy.
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- zumaΒ·03-18 09:58thx1Report