NVIDIA Corporation
Leading designer of graphics processing units (GPUs) for gaming, professional visualization, data centers, and automotive markets
What the page says before deeper research
Quality, growth, value, ownership, risk, and source confidence.
Moat 95/100, high switching costs, 165% NRR.
Growth appears healthy from +65% YoY revenue growth.
Forward P/E of 22.0x versus +65% growth gives a 0.3x multiple-to-growth read.
Institutional distribution risk from 2026 Q1 filings; treat it as delayed regulatory data.
Retention and AI disruption risks are low; valuation is not flagged expensive.
Fundamentals from finnhub as of 2026-05-17 plus SEC-backed 13F metadata support this read.
This is the clearest company-level growth signal available in the provided fundamentals.
Beginner valuation check
Data pending from FMP or Finnhub.
Positive price performance shows recent market sentiment, not a full investment thesis.
Forward P/E around 22.0x means investors pay about $22.0 for each expected $1 of future profit per share, usually the next 12 months or next fiscal year. It is a forecast, not a fact.
A P/E around 45.6x means investors pay about $45.6 for each $1 the company earned per share over the last 12 months, usually the last four quarterly reports.
Source: market data index. As of May 21, 2026. P/E can be unavailable or misleading when earnings are negative.
Beginner guide
NVIDIA makes the special computer chips (GPUs) that create video game graphics and now power AI and robots. They're like the brain that helps computers think really fast.
Leading designer of graphics processing units (GPUs) for gaming, professional visualization, data centers, and automotive markets
NVIDIA Corporation makes money through Data Center (~78% of revenue), Gaming (~15% of revenue), and Automotive & Robotics (~3% of revenue).
CUDA ecosystem creates 10+ year switching costs - enterprises can't easily migrate millions of lines of CUDA code
NVIDIA Corporation can disappoint if execution, competition, valuation, or demand cycles weaken growth, margins, customer retention, or investor confidence.
NVIDIA Corporation is like a business operating system for data center: customers pay because replacing it can be disruptive.
You are basically betting that NVIDIA Corporation can keep turning data center into durable value while managing execution, competition, valuation, or demand cycles.
A 0-100 shortcut for how defensible the business looks in this company brief. NVIDIA Corporation is scored at 95.
How painful it is for customers to leave. this company brief rates NVIDIA Corporation as high.
Whether existing customers tend to spend more or less over time. The company brief model uses 165%.
The main pieces of the company here are Data Center, Gaming, and Automotive & Robotics.
Price divided by earnings. It is a quick valuation check, but it can mislead when earnings are temporarily high, low, or negative.
A quarterly filing that shows what many large institutional investors owned at quarter end.
The first four questions
NVIDIA’s Data Center business is the main engine here, with AI training and inference GPUs, DGX systems, networking, and CUDA software supporting the core growth story. The CUDA ecosystem and hardware-software stack make it harder for customers to switch.
NVDA can disappoint if execution, competition, valuation, or demand cycles weaken growth, customer retention, or investor confidence. The 13F read is not real-time and may miss intra-quarter position changes or derivatives.
Forward P/E (22.0x)
Next earnings date unavailable from configured sources.
Bull / Neutral / Bear
NVIDIA keeps converting AI demand into durable data-center revenue while investors weigh a still-elevated but much lower forward multiple than trailing earnings suggest.
Forward P/E around 22.0x remains reasonable versus growth and business quality.
Data Center stays the dominant engine and customers keep building around CUDA, which preserves NVIDIA’s switching costs and supports continued revenue expansion.
Revenue growth remains strong and the forward P/E stays anchored by that growth.
NVIDIA keeps converting AI demand into durable data-center revenue while investors weigh a still-elevated but much lower forward multiple than trailing earnings suggest.
Forward P/E around 22.0x remains reasonable versus growth and business quality.
If competition, execution, or demand cycles slow the data-center machine, the market may question how much growth is already priced in, while 13F filings continue to arrive with a lag.
13F filings show more reductions or exits, and revenue growth cools from current levels.
Beginner checklist
Needs earnings calendar data from a provider.
This is the clearest company-level growth signal available in the provided fundamentals.
Margin trend needs financial statement data and should not be inferred from price action.
A useful valuation anchor, but it should be judged against NVIDIA’s growth and data-center durability.
Latest imported 13F data shows more reported reductions than new accumulation strength overall.
Insider activity was not provided and should be treated as unavailable.
For NVIDIA, start by tracking whether the data-center engine is strengthening or weakening.
NVIDIA Corporation is exposure to technology - semiconductors operating model with high switching costs and 165% net revenue retention.
CUDA ecosystem creates 10+ year switching costs - enterprises can't easily migrate millions of lines of CUDA code
The main question is whether the company can keep customer value compounding without margin pressure eroding the moat.
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Open the 13F workspace to inspect holders, changes, source quality, and investor signals.
- CUDA ecosystem creates 10+ year switching costs - enterprises can't easily migrate millions of lines of CUDA code
- Custom silicon design requires 3-5 years to replicate, giving NVIDIA sustained competitive advantage
- AI training workloads are performance-sensitive; customers pay premium for 2-3x faster training times
- Developer mindshare is sticky - 4M+ CUDA developers create self-reinforcing network effects
- Hardware-software co-optimization (TensorRT, cuDNN) makes competitive displacement extremely difficult