NVIDIA (NVDA): FY27 Q1 earnings call, May 20, 2026
On its May 20, 2026 earnings call (FY27 Q1), NVIDIA said 15 of the 43 words Kalshi and Polymarket listed; Parrot’s count agreed with all 43 settlements. Quotes below are at most 25 words each, with timed clips.
UpdatedMarkets
43 words · 15 settled yes · our count agreed on 43 of 43- Kalshi: company speakers + operator
Kalshi - Polymarket: anyone
Polymarket
backtest The probabilities on settled rows are a walk-forward backtest: the model as of before each event, computed after Parrot went live; they were not published at the time.
| Word | Result | Our count | All speakers | Agrees | Probability | Clip | Market |
|---|---|---|---|---|---|---|---|
| AI 30+ times | Yes | 104 | 104 | ✓ | 85%transcripts from the transcript model | Hear it 2:47, AI 30+ times | – |
| Agent / Agentic 10+ times | Yes | 26 | 26 | ✓ | 68%transcripts from the transcript model | Hear it 5:23, Agent / Agentic 10+ times | – |
| Data Center 20+ times | Yes | 24 | 24 | ✓ | 59%transcripts from the transcript model | Hear it 3:16, Data Center 20+ times | – |
| Vara / Rubin 10+ times | Yes | 23 | 23 | ✓ | 50%transcripts from the transcript model | Hear it 10:23, Vara / Rubin 10+ times | – |
| Token 20+ times | Yes | 22 | 22 | ✓ | 34%transcripts from the transcript model | Hear it 3:52, Token 20+ times | – |
| Blackwell 10+ times | Yes | 20 | 20 | ✓ | 80%transcripts from the transcript model | Hear it 2:40, Blackwell 10+ times | – |
| Anthropic | Yes | 9 | 9 | ✓ | 84%transcripts from the transcript model | Hear it 9:03, Anthropic | – |
| Hyperscaler | Yes | 8 | 11 | ✓ | 86%n=2+tx from settlement history blended with transcripts | Hear it 2:45, Hyperscaler | – |
| OpenAI | Yes | 7 | 7 | ✓ | 90%transcripts from the transcript model | Hear it 9:04, OpenAI | – |
| Incredible / Unprecedented | Yes | 5 | 5 | ✓ | 90%transcripts from the transcript model | Hear it 7:09, Incredible / Unprecedented | – |
| Meta | Yes | 2 | 2 | ✓ | 86%transcripts from the transcript model | Hear it 11:12, Meta | – |
| Self Driving | Yes | 2 | 2 | ✓ | 78%n=2+tx from settlement history blended with transcripts | Hear it 23:18, Self Driving | – |
| Silicon / Semiconductor | Yes | 2 | 2 | ✓ | 67%transcripts from the transcript model | Hear it 54:55, Silicon / Semiconductor | – |
| Gaming | Yes | 1 | 1 | ✓ | 90%n=3+tx from settlement history blended with transcripts | Hear it 5:27, Gaming | – |
| Humanoid | Yes | 1 | 1 | ✓ | 35%n=2+tx from settlement history blended with transcripts | Hear it 16:05, Humanoid | – |
| Omniverse | No | 0 | 0 | ✓ | 88%n=4+tx from settlement history blended with transcripts | – | |
| Gigawatt | No | 0 | 0 | ✓ | 86%transcripts from the transcript model | – | |
| Open Source | No | 0 | 0 | ✓ | 80%transcripts from the transcript model | – | |
| H20 | No | 0 | 0 | ✓ | 75%n=2+tx from settlement history blended with transcripts | – | |
| Cosmos | No | 0 | 0 | ✓ | 72%n=3+tx from settlement history blended with transcripts | – | |
| Gigascale | No | 0 | 0 | ✓ | 56%transcripts from the transcript model | – | |
| Superintelligence | No | 0 | 0 | ✓ | 56%transcripts from the transcript model | – | |
| TSMC | No | 0 | 0 | ✓ | 54%n=2+tx from settlement history blended with transcripts | – | |
| Talent | No | 0 | 0 | ✓ | 51%n=2+tx from settlement history blended with transcripts | – | |
| EU / Europe | No | 0 | 0 | ✓ | 49%transcripts from the transcript model | – | |
| GPU In Space / GPUs in Space | No | 0 | 0 | ✓ | 41%transcripts from the transcript model | – | |
| Automation | No | 0 | 0 | ✓ | 32%n=2+tx from settlement history blended with transcripts | – | |
| Trump | No | 0 | 0 | ✓ | 29%n=3+tx from settlement history blended with transcripts | – | |
| Taiwan | No | 0 | 0 | ✓ | 29%n=3+tx from settlement history blended with transcripts | – | |
| Bubble | No | 0 | 0 | ✓ | 28%transcripts from the transcript model | – | |
| Disney | No | 0 | 0 | ✓ | 28%transcripts from the transcript model | – | |
| Arizona | No | 0 | 0 | ✓ | 23%n=2+tx from settlement history blended with transcripts | – | |
| AGI | No | 0 | 0 | ✓ | 22%transcripts from the transcript model | – | |
| Domestic | No | 0 | 0 | ✓ | 21%n=2+tx from settlement history blended with transcripts | – | |
| Tariff | No | 0 | 0 | ✓ | 21%n=2+tx from settlement history blended with transcripts | – | |
| GM / General Motors | No | 0 | 0 | ✓ | 19%n=2+tx from settlement history blended with transcripts | – | |
| AMD | No | 0 | 0 | ✓ | 18%n=1+tx from settlement history blended with transcripts | – | |
| Circular | No | 0 | 0 | ✓ | 17%transcripts from the transcript model | – | |
| Ethereum | No | 0 | 0 | ✓ | 17%transcripts from the transcript model | – | |
| Layoff | No | 0 | 0 | ✓ | 17%transcripts from the transcript model | – | |
| Nanometer | No | 0 | 0 | ✓ | 17%transcripts from the transcript model | – | |
| TPU | No | 0 | 0 | ✓ | 17%transcripts from the transcript model | – | |
| VR / Virtual Reality | No | 0 | 0 | ✓ | 11%n=2+tx from settlement history blended with transcripts | – |
Market prices are mid quotes from Kalshi and Polymarket, shown for context, may be delayed by several minutes and are not a signal. Quotes as of Oct 21, 2026 04:22 UTC.
Transcript
quotes of at most 25 wordsSpeaker-labelled transcript · Source and credits · Used to count words; quotes of at most 25 words.
12 matches for “Enterprise”; 5 quoted, at least 25 words apart.
…report two sub-markets, Hyperscale and ACIE, which incorporates AI clouds, industrial, and enterprise. Hyperscale will include revenue from the public cloud and the world's largest…
The second thing is the applications are diverse. It could be in enterprise, it could be in the energy sector, manufacturing sector and such. Where…
Some environments just require an enterprise, for example, require a company who has all of the technologies working together so that they don't have to…
…their public clouds. That's one segment. The second segment is AI natives, enterprise on-prems, industrial on-prems, and sovereign AI. That segment is growing incredibly fast…
…They're regional. They're all over the place. There are startups all over the world supporting those companies. They're enterprise, 250,000 enterprise companies around the world.
Where the listed words were said
…across our diverse end customer base, from hyperscalers to model makers to AI cloud providers and sovereign customers. In Q1, we also allocated capital effectively…
…witnessed mainstream AI transition from one-shot inference to reasoning and to now agentic. AI is no longer a nice-to-have. AI is now a necessity for…
This is critical to the market's development and our long-term position. Data center revenue of $75 billion was up 92% year-over-year and 21% sequentially, driven…
…Starting this year, AWS will add more than 1 million Blackwell and Rubin GPUs and are collaborating on Spectrum-X networking. At Google, Blackwell will be…
…Grace Blackwell is the fastest training system as well as the lowest token generation cost at inference. Spectrum-X, our end-to-end Ethernet platform purpose-built for AI,…
…reach $3 trillion-$4 trillion annually by the end of this decade. Our Blackwell architecture is everywhere, adopted and deployed by every major hyperscaler, every cloud…
…through AWS, Azure, CoreWeave, SpaceXAI, and more. Now, with the addition of Anthropic, too, OpenAI, Gemini, SpaceXAI, Meta, MSL, Microsoft AI, TML, Reflection, Perplexity, Cursor,…
…in each one of them. The easiest go-to-market, of course, is the hyperscaler, because there are only five or six of them. The rest of…
…layer, particularly at Anthropic and OpenAI, has been incredible, with momentum continuing to accelerate, including breakout growth in OpenAI's, Codex since the launch of GPT-5.5.
…AI computing platform. Demand for AI infrastructure continues to expand at an unprecedented pace. The build-out of AI factories is accelerating. The value of NVIDIA…
…model. With the addition of Anthropic to our existing partners, OpenAI, xAI, Meta and Mistral AI, Gemini and many others, our share of frontier AI…
…centers and the edge. Edge including, of course, what most people see, self-driving cars, robotics, but a large growing network of computers inside manufacturing plants,…
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