NVIDIA remains the center of AI training and high-end inference demand.
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Price
$215.66
1D change
-0.73%
Market cap
$5.21T
Sector
Technology
Instinct GPUs give hyperscalers a second-source path for AI training and inference.
Price
$536.01
1D change
-4.63%
Market cap
$875.02B
Sector
Technology
ASML is the sole supplier of EUV lithography systems used for the most advanced logic and memory nodes.
Price
$1.7K
1D change
+0.14%
Market cap
$637.46B
Sector
Technology
Most leading AI chips still depend on TSMC's manufacturing and advanced packaging.
Price
$413.33
1D change
-2.06%
Market cap
$2.14T
Sector
Technology
| Metric | NVDA | AMD | ASML | TSM |
|---|---|---|---|---|
| Price | $215.66 | $536.01 | $1.7K | $413.33 |
| 1D Change | -0.73% | -4.63% | +0.14% | -2.06% |
| Market Cap | $5.21T | $875.02B | $637.46B | $2.14T |
| Enterprise Value | $5.21T | $908.99B | $35.27T | $15.43T |
| Trailing P/E | 27.3 | 136.7 | 40.8 | 26.8 |
| Forward P/E | 13.6 | 34.0 | 26.7 | 17.9 |
| PEG Ratio | 0.27x | 0.57x | 1.03x | 0.81x |
| Price / Sales | 17.2 | 21.2 | 16.1 | 0.5 |
| EV / Revenue | 17.2 | 22.0 | 893.4 | 3.5 |
| Revenue Growth | 105.9% | 50.1% | 55.2% | 36.0% |
| Earnings Growth | 127.8% | 159.5% | 135.9% | 77.4% |
| Gross Margin | 74.7% | 55.7% | 54.3% | 64.2% |
| Operating Margin | 66.2% | 17.2% | 44.4% | 60.3% |
| Net Margin | 63.7% | 15.6% | 34.1% | 49.9% |
| ROE | 117.2% | 10.2% | 65.6% | 40.0% |
| Free Cash Flow | $41.81B | $8.84B | $10.37B | $730.83B |
| FCF Margin | 13.8% | 21.4% | 26.3% | 16.5% |
| Debt / Equity | 0.17x | 6.36x | 8.90x | 0.17x |
| Current Ratio | 4.59x | 2.61x | 1.49x | 2.46x |
| Dividend Yield | 0.46% | 0.00% | 0.52% | 1.07% |
| Next Earnings | Nov 17, 2026 | Nov 03, 2026 | — | Jan 14, 2027 |
| Quarterly Revenue | $96.22B | $11.54B | $9.33B | $1.27T |
| Revenue QoQ | +17.9% | +12.5% | +6.4% | +12.0% |
| Quarterly Net Income | $59.69B | $2.30B | $2.92B | $706.56B |
| Net Income QoQ | +2.3% | +66.1% | +5.8% | +23.4% |
NVDA thesis lens
AI compute platform
Why it could benefit
- NVIDIA remains the center of AI training and high-end inference demand.
- Its stack includes chips, networking, systems, CUDA, and software libraries, not just GPUs.
- As models get larger and enterprises move into production, full-stack control becomes more valuable.
Moat / edge
- CUDA ecosystem and developer lock-in.
- Leading performance in accelerated computing.
- Integrated platform spanning silicon, interconnect, and software.
What to watch
- Supply-demand balance for each new architecture cycle.
- Mix shift between hyperscalers and enterprise customers.
- Competition from custom silicon and AMD.
Key risks
- Customer concentration and product-transition execution matter a lot.
- Any sharp slowdown in capex could compress expectations quickly.
AMD thesis lens
GPU/CPU AI accelerator challenger
Why it could benefit
- Instinct GPUs give hyperscalers a second-source path for AI training and inference.
- EPYC CPUs and adaptive compute help AMD participate across the AI server bill of materials.
- If open accelerator ecosystems broaden, AMD can gain share from customers avoiding single-vendor dependence.
Moat / edge
- High-performance CPU franchise and growing GPU roadmap.
- Deep relationships with hyperscalers, OEMs, and enterprise server buyers.
- Chiplet design capability and manufacturing partnership with TSMC.
What to watch
- Instinct shipment ramp and software maturity.
- Gross margin mix between CPUs, GPUs, gaming, and embedded.
- Cloud customer adoption versus Nvidia alternatives.
Key risks
- Nvidia's software ecosystem remains the hardest competitive hurdle.
- AI GPU demand can be lumpy if customer qualification slips.
ASML thesis lens
EUV lithography chokepoint
Why it could benefit
- ASML is the sole supplier of EUV lithography systems used for the most advanced logic and memory nodes.
- AI compute demand raises the need for leading-edge wafer capacity.
- Memory ramps such as advanced DRAM and HBM also pull on lithography intensity.
Moat / edge
- Unique EUV technology stack with deep supplier specialization.
- Entrenched customer relationships with leading-edge foundries and memory makers.
- Long development cycles make displacement extremely difficult.
What to watch
- EUV and High NA EUV order trends.
- China restrictions and shipment timing.
- Foundry and memory capex cycles.
Key risks
- Export controls can limit sales into key regions.
- Semicap cycles can create long drawdowns despite strong long-term positioning.
TSM thesis lens
Advanced semiconductor manufacturing
Why it could benefit
- Most leading AI chips still depend on TSMC's manufacturing and advanced packaging.
- As AI complexity rises, foundry leadership in yield, scale, and packaging matters more.
- TSMC is a cleaner way to own the ecosystem rather than one end-market winner.
Moat / edge
- Leading-edge process technology.
- Manufacturing scale and execution track record.
- Hard-to-replicate ecosystem trust with top chip designers.
What to watch
- Capacity additions in advanced nodes and CoWoS-type packaging.
- Geographic expansion and margin preservation.
- Mix between smartphone, HPC, and AI demand.
Key risks
- Geopolitical risk is always part of the TSMC thesis.
- Large customer concentration can amplify cycle swings.