u/bl0797

It's crazy that so many people still think Nvidia is just a chip company. Unfortunately, I think this kind of public sentiment that is delaying/stopping the data center buildout is a huge risk to Nvidia's stock price.

Here's a ChatGPT summary of the recurring themes in 500+ comments on Stephen Witt's NYT article about Nvidia CEO Jensen Huang, following his Ezra Klein interview:

• Jensen understands hardware, but does he understand AI software? Many commenters argue that Huang's expertise in GPUs and computing infrastructure doesn't necessarily translate into understanding neural networks, emergent intelligence, or AI safety.
• Predictable components don't mean predictable AI. Critics argue that knowing every mathematical operation performed by a neural network doesn't mean you can predict or control the behavior of the entire system.
• Huang has an enormous financial conflict of interest. Nvidia profits from accelerating AI development, giving Huang strong incentives to minimize concerns about its risks.
• Not every problem is an engineering problem. Commenters criticize Huang's belief that virtually any challenge can be solved through engineering, arguing that AI also presents ethical, societal, and potentially uncontrollable risks.
• His confidence about AI safety is unconvincing. Many readers believe Huang dismisses existential risks without providing sufficient technical evidence that advanced AI can be reliably controlled.
• His relationship with Trump raises concerns. Commenters criticize Huang's apparent use of flattery and political influence to discourage AI regulation and advance Nvidia's business interests.
• The free market won't necessarily protect the public. Readers reject Huang's argument that profit incentives alone will ensure AI safety, pointing to the history of corporations prioritizing growth over public welfare.
• The Ezra Klein interview damaged Huang's credibility. Commenters describe his explanations as simplistic, evasive, or overconfident, particularly when discussing AI safety and the limits of engineering.
• A minority defend Huang: Supporters praise his technical expertise, optimism, and practical engineering perspective. Some consider existential AI fears exaggerated and argue that AI remains a controllable technology with enormous potential benefits.

Bottom line: The dominant criticism is that Huang is an exceptionally successful hardware executive whose financial interests and engineering worldview may lead him to underestimate AI risks. Many commenters question whether his expertise qualifies him to make sweeping assurances about AI software and safety. A minority defend his engineering perspective and believe AI risks are exaggerated.

The latest MLPerf Inference benchmark tests were just released. Results are released quarterly, alternating between inference and training.

Looks like Nvidia is firing on all cylinders - Vera Rubin is meeting performance expectations and volume deliveries are underway.

From the blog post:

• In its first MLPerf Inference preview submission, NVIDIA Vera Rubin NVL72 delivers up to 3.7x better throughput than GB300 NVL72.

• NVIDIA GB300 NVL72 scales with leading efficiency: A 288-GPU submission across four GB300 NVL72 racks achieved 99% scaling efficiency, with throughput growing nearly linearly from a single-rack baseline.

• Continuous software optimizations drive performance gains: Software optimizations in NVIDIA’s MLPerf Inference v6.1 submissions delivered up to 1.6x higher performance over v6.0. Optimizations continued post-v6.1 submission, delivering further performance gains.

Huge win for Nvidia if Apple licenses NVLink Fusion:

"Apple is developing an enterprise AI server built around its own M8 Ultra chips, with a target launch of 2029, and has held talks with Nvidia about using the chipmaker’s NVLink Fusion interconnect platform to link those chips together, The Information reported Wednesday."

"The planned server would come in two configurations — one pairing two M8 Ultra chips and one pairing four — and would target AI developers, businesses and governments, according to The Information. Some Apple engineers working on the project believe Nvidia’s NVLink Fusion, which bundles switches, chiplets and software to allow chips from different vendors to communicate at high speed, represents the best available connectivity solution"

Michael Dell on X: "We have delivered the world's first production @nvidia Vera Rubin NVL72 racks to @CoreWeave. Special thanks to our @DellTech teams who make it happen."

While competitors make big promises, Nvidia delivers AI Factories.

The 67^(th) edition of the TOP500 Supercomputer list, updated semi-annually, was released today.

https://top500.org/lists/top500/2026/06/highs/

https://top500.org/lists/top500/2026/06/

HIGHLIGHTS:

• LineShine from China takes the # 1 spot using LX2 304C processors and LingQi interconnects.
• 277 supercomputers use GPUs/accelerators, up from 255 six months ago.
• Nvidia gpus are used in 238, up from 219 in 11/2025, 201 in 5/2025, 184 in 11/2024, 172 in 6/2024, 166 in 11/2023.
• AMD gpus are used in 32, up from 29 in 11/2025, 27 in 5/2025, 19 in 11/2024, 14 in 6/2024, 11 in 11/2023.
• Nvidia Infiniband is the most-used networking interconnect with 293, up from 277 in 11/2025, 271 in 5/2025, 253 in 11/2024, 238 in 6/2024, 218 in 11/2023.
• On the Green500 list (ranked by performance/watt), Nvidia Hopper-based systems take the top 8 spots with GH200 systems placing 1,2,3,4,8, H100 placing 5,6,7, and AMD MI300A systems in spots 9th and 10th.

=======================================================

6/2026 TOP500 details

GPUs

• 238 use Nvidia - 19 use B200, 55 use H200, 77 use H100, 87 use Ampere and older
• 32 use AMD - 2 with MI355X, 2 with MI300X, 13 with MI300A, 15 with MI200 series
• 4 use Intel

CPUs

• 265 use Intel
• 191 use AMD
• 26 use Nvidia Grace

NETWORKING

• 293 use Nvidia Infiniband/Spectrum-X
• 164 use Gigabit Ethernet
• 16 use Intel Omni-Path

=======================================================

11/2025 TOP500 details

GPUs

• 219 use Nvidia - 10 use B200, 38 use H200, 76 use H100, rest are Ampere and older
• 29 use AMD - 2 with MI300X, 11 with MI300A, 16 with MI200 series
• 4 use Intel

CPUs

• 287 use Intel
• 177 use AMD
• 18 use Nvidia Grace

NETWORKING

• 279 use Nvidia InfiniBand/Spectrum-X
• 169 use Gigabit Ethernet
• 30 use Intel Omni-Path

=================================================

6/2025 TOP500 details

GPUs

• 201 use Nvidia - 25 H200, 70 use H100, rest are Ampere and older
• 27 use AMD - 1 with MI300X, 10 with MI300A, 16 with MI200 series
• 6 use Intel

CPUs

• 294 use Intel
• 173 use AMD
• 13 use Nvidia Grace

NETWORKING

• 273 use Nvidia - 271 with InfiniBand, 2 with Spectrum-X
• 169 use Gigabit Ethernet

=================================================

11/2024 TOP500 details

GPUs

• 184 use Nvidia, including 8 with GH200, 2 with H200, 56 with H100, 70 with A100
• 19 use AMD, including 1 with MI300X, 5 with MI300A
• 5 use Intel

CPUs

• 318 use Intel
• 162 use AMD
• 9 use Nvidia Grace

NETWORKING

• 253 use Nvidia InfiniBand
• 184 use Gigabit Ethernet

======================================================

6/2024 TOP500 details

GPUs

• 172 use Nvidia, including 7 with GH200, 21 with H100, 81 with A100
• 14 use AMD, including 3 with MI300A
• 5 use Intel

CPUs

• 325 use Intel
• 157 use AMD
• 7 use Nvidia Grace

NETWORKING

• 238 use Nvidia InfiniBand
• 194 use Gigabit Ethernet

=========================================================

11/2023 TOP500 details

GPUs

• 166 use Nvidia gpus, including 10 with H100, 110 with A100
• 11 use AMD gpus, all from MI200 series
• 7 use Intel gpus

CPUs

• 339 use Intel
• 150 use AMD
• 0 use Nvidia Grace

NETWORKING

• 218 use Nvidia InfiniBand
• 210 use Gigabit Ethernet

The competition delivers promises. Nvidia delivers results. What else is new?

"In MLPerf Training 6.0 — the latest of a series of rigorous, peer-reviewed industry benchmarks for evaluating AI training performance — the NVIDIA Blackwell platform led across every category, demonstrating:

• Fastest time to train on every benchmark

• Largest-scale training across 8,192 GPUs using NVIDIA Blackwell NVL72 systems

• The only platform with submissions across all seven benchmarks in the suite

NVIDIA brings together performance, scale and reliability in a single platform engineered through extreme codesign to enable AI model builders to launch frontier models faster, minimize training costs and start generating revenue early."

The ranking seeks to evaluate how well companies in the S&P 500 are doing on measures intended to match up with future success. The companies were scored on 30 metrics from 20 data providers across six areas:

• AI readiness

• Innovation more broadly

• Talent readiness (based on hiring, retention and employee satisfaction)

• Financial fitness

• Supply-chain resilience and exposure to geopolitical and other risks

• Corporate agility, including executive share of total head count and how employees regard their company.

Of the six areas, Nvidia was ranked # 1 in AI Readiness and Agility, # 2 in Innovation, Talent Readiness and Financial Fitness, and # 110 in Resilience.

The top 15 overall scores:

1. Nvidia - 97.9
2. Alphabet - 91
3. Microsoft - 81.2
4. Meta - 81.2
5. Cisco - 78.9
6. Salesforce - 77.7
7. Mastercard - 75.7
8. Amazon - 75.3
9. Adobe - 74.5
10. Intuit - 74.3
11. ServiceNow - 74.1
12. Apple - 74.1
13. S&P Global - 71.1
14. Arista - 71.1
15. Visa - 70.9

Jensen does a 70 minute Q&A at a Stanford computer science class. He is more opinionated and animated than usual here, could be a trend starting with the Dwarkesh Patel interview?

Some highlights:

• Speaking on extreme co-design, Grace Blackwell was designed for inference and decode, Vera Rubin was designed for high cpu performance for agents, Feynman is designed for swarms of agents and sub-agents.

• Nvidia consumes more OpenAI and Anthropic tokens than anyone.

• Cybersecurity will be solved by having "millions, billions, swarms of cheap AIs and we're going to systematically surround it."

• On gpus for China, gpus are not like nuclear weapons. "It makes no sense in this moment is to compare Nvidia GPUs to atomic bombs. There are a billion people with Nvidia GPUs. I advocate Nvidia GPUs to all of you. I advocate Nvidia GPUs to my family, to my kids, to people I love, but I don't advocate atomic bombs to anybody. So that analogy is stupid."

• Nvidia's two biggest failures were NV-1 (the first chip released back in 1995, "the architechure, the technology we used was completely wrong") and pursuing a mobile SOC (Tegra). Turns out that communication features were much more important than cpu and gpu features, and Qualcomm owned that, so Nvidia was locked out. The consolation is that working on low-power tech has led to Nvidia's auto/robotics products.

• Asked about Stanford's severe shortage of tokens available for Stanford students and researchers (and almost every other university), Jensen said it's not a chip shortage problem, it's a Stanford planning problem. Stanford needs to spend $1B of its $40B endowment to build its own data center and/or rent cloud time.

• Jensen's favorite orders at Denny's are the fried chicken, the Superbird, and grilled ham and cheese with tomato and mustard.