Silicon Investor - NVIDIA Corporation (NVDA)

[Frank Sully] Here’s a summary of Jensen Huang’s keynote at NVIDIA GTC Washington, D.C. on Oct...

Frank Sully — 10/31/2025 5:56:05 PM

Here’s a summary of Jensen Huang’s keynote at NVIDIA GTC Washington, D.C. on October 28, 2025:

Leadership in AI & Accelerated Computing: Huang opened by emphasizing how NVIDIA’s innovations are driving a paradigm shift across industries, with “AI factories” (AI-optimized data centers) set to transform computing. NVIDIA’s Blackwell GPUs remain at the forefront for extreme performance.?

New Products & Roadmap: Key launches included the Blackwell Ultra NVL72 platform, designed for highly efficient AI inference, training, and reasoning. Huang previewed future architectures—Vera Rubin (2026) and Rubin Ultra (2027)—offering over 3x the performance of Blackwell Ultra for demanding workloads.?

Quantum–AI Integration: NVIDIA unveiled NVQLink, a new system architecture to connect quantum computers with NVIDIA’s GPUs. This tech aims to accelerate breakthroughs in medicine and materials science by enabling powerful quantum/AI hybrid supercomputers.?

Robotics Revolutions: Huang introduced the open Isaac GROOT N1 Humanoid Robot foundation model for rapid, cost-effective robotics development. Partnerships with DeepMind and Disney Research will bring advanced robotics and physics-driven simulations to real products.?

Enterprise & Developer Tools: Announcements included DGX Spark and Grace Blackwell-powered DGX Stations, providing enterprises, researchers, and students with local and cloud options for prototyping and fine-tuning large models.?

Omniverse & Digital Twins: New Omniverse projects like Cosmos enable customizable “physical AI” world foundation models, creating digital twins for optimized workflows in manufacturing, logistics, and more.?

Autonomous Vehicles: NVIDIA reaffirmed its leadership in AVs with partnerships (GM, Tesla, Waymo) and new AI-focused collaborations to expand global adoption and manage massive data workloads.?

US Manufacturing & Policy: Huang confirmed NVIDIA’s top AI chips are now being manufactured in Arizona, marking a step towards domestic supply chain resilience, and highlighted NVIDIA’s intention to collaborate further with government and contractors.?

China Market Update: Huang addressed the importance of China, aiming for $50 billion in sales, and discussed balancing innovation and geopolitical realities.?

Closing note: Jensen positioned NVIDIA as the key enabler powering the next era of AI—from quantum computing and sovereign AI infrastructure to robotics and autonomous everything.?

[Frank Sully] New Sensor Frontiers From Jestson Thor Robotic Platform The availability of Nvi...

Frank Sully — 9/1/2025 7:14:14 PM

New Sensor Frontiers From Jestson Thor Robotic Platform

The availability of Nvidia’s Jetson Thor redefines what’s possible for humanoids and autonomous mobile robots (AMRs) by fusing physical intelligence and real-time reasoning through robotics foundation models.

The new compute platform for robots comprises a Blackwell GPU, transformer engine, multi-instance GPU (MIG), a 14-core Arm Neoverse V3AE CPU, and up to 128 GB of LPDDR5X memory.

The platform’s high-throughput I/O, including 4×25 GbE, provides the bandwidth needed to fuse dense multimodal sensing in real-time. Nvidia’s Jetson Thor, a Blackwell-powered robotics computer, is the first platform to run robotics foundation models at scale; these models compress decades of challenges into perception-rich humanoids capable of dexterous, human-speed manipulation.

However, the real breakthrough, according to Paul Golding, VP of Edge AI at Analog Devices Inc., is reasoning that integrates multimodal inputs to plan, adapt, and act in real-time. “For the first time, robots can understand complex tasks.”

Next, Sebastian Scherer, an associate research professor at Carnegie Mellon’s Robotics Institute, points out that there has been a big disconnect between computer vision and robotics because computer vision workloads were too slow for real-time decision-making. With Jetson Thor, models and computing have become fast enough, allowing robots to handle much more nuanced tasks.

Jetson’s ecosystem—which encompasses Holoscan Sensor Bridge and Isaac Sim—supports a variety of application requirements, high-speed industrial automation protocols, and sensor interfaces. Holoscan Sensor Bridge is a sensor-over-Ethernet technology designed to enable real-time data streaming and simplify high-speed sensor fusion and actuator integration on Nvidia’s edge AI platforms.

Sensor and actuator companies—including ADI, e-con Systems, Infineon, Leopard Imaging, and RealSense—are using this platform to connect sensor data from cameras, radar, and lidar, and send it directly to GPU memory on Jetson Thor with ultra-low latency.

Isaac Sim is an open-source reference application that enables developers to simulate and test AI-driven robotics solutions in physically based virtual environments. ADI is embedding robotics foundation models into its development stack so that its hardware behaves in Nvidia Isaac Sim as it will in the real world.

“Our goal is to build the most physically accurate robotics content in Nvidia Isaac Sim, enabling teams to iterate at simulation speed and then scale seamlessly to real systems with ADI hardware and Nvidia Jetson Thor,” said ADI’s Golding.

In this case, physical intelligence fuses sensing, actuation, policy learning, and reasoning to enable robots to execute precise industrial tasks. At the same time, however, it demands high-fidelity edge sensing, energy-efficient and functionally safe power, and deterministic connectivity to central compute.

That opens the doors for new opportunities for analog chip firms with expertise in edge sensing, precision motion control, power integrity, and deterministic connectivity. When combined with Jetson Thor’s compute capabilities, these analog building blocks can accelerate the development of humanoids and AMRs.

[Frank Sully] NVIDIA Robotic Stack Used By Over 2M Developers seekingalpha.com As CFO Colett...

Frank Sully — 8/18/2025 8:18:44 PM

NVIDIA Robotic Stack Used By Over 2M Developers

seekingalpha.com

As CFO Colette Kress said during Q1 Earning Call:

The era of robotics is here, billions of robots, hundreds of millions of autonomous vehicles and hundreds of thousands of robotic factories and warehouses will be developed.

[Frank Sully] SANTA CLARA, Calif., July 30, 2025 (GLOBE NEWSWIRE) -- NVIDIA (NVDA) will host a...

Frank Sully — 8/17/2025 6:58:36 PM

SANTA CLARA, Calif., July 30, 2025 (GLOBE NEWSWIRE) -- NVIDIA (NVDA) will host a conference call on Wednesday, August 27, at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the second quarter of fiscal year 2026, which ended July 27, 2025.

The call will be webcast live (in listen-only mode) on investor.nvidia.com. The company’s prepared remarks will be followed by a Q&A session, which will be limited to questions from financial analysts and institutional investors.

Ahead of the call, NVIDIA will provide written commentary on its second-quarter results from Colette Kress, the company’s executive vice president and chief financial officer. This material will be posted to investor.nvidia.com immediately after the company’s results are publicly announced at approximately 1:20 p.m. PT.

The webcast will be recorded and available for replay until the company’s conference call to discuss financial results for its third quarter of fiscal year 2026.

About NVIDIA
NVIDIA (NASDAQ: NVDA) is the world leader in accelerated computing.

[Frank Sully] Jensen Huang's 2025 Paris GTC Keynote (1.5 hours) youtube.com

Frank Sully — 6/11/2025 9:34:36 PM

[Frank Sully] Highlights from 1Q Earnings Call CFO Collette Kress on AI factories: The pace ...

Frank Sully — 5/28/2025 9:24:32 PM

Highlights from 1Q Earnings Call

CFO Collette Kress on AI factories:

The pace and scale of AI factory deployments are accelerating with nearly 100 NVIDIA-powered AI factories in flight this quarter, a two-fold increase year-over-year, with the average number of GPUs powering each factory also doubling in the same period. And more AI factory projects are starting across industries and geographies. NVIDIA's full stack architecture is underpinning AI factory deployments as industry leaders like AT&T, BYD, Capital One, Foxconn, MediaTek, and Telenor, are strategically vital sovereign clouds like those recently announced in Saudi Arabia, Taiwan and the UAE. We have a line of sight to projects requiring tens of gigawatts of NVIDIA AI infrastructure in the not-too-distant future.

The transition from generative to agentic AI, AI capable of receiving, reasoning, planning and acting will transform every industry, every company and country. We envision AI agents as a new digital workforce capable of handling tasks ranging from customer service to complex decision-making processes.

CFO Collette Kress on Autonomous Automotive and Robotics:

Lastly with our Automotive Group. Revenue was $567 million, down 1% sequentially, but up 72% year-on-year. Year-on-year growth was driven by the ramp of self-driving across a number of customers and robust end demand for NEVs. We are partnering with GM to build the next-gen vehicles, factories and robots using NVIDIA AI, simulation and accelerated computing. And we are now in production with our full stack solution for Mercedes-Benz starting with the new CLA, hitting roads in the next few months.

We announced Isaac GR00T N1, the world's first open fully customizable foundation model for humanoid robots, enabling generalized reasoning and skill development. We also launched new open NVIDIA Cosmos World Foundation models. Leading companies include 1X, Agility Robots -- Robotics, Figure AI, Uber and Waabi. We've begun integrating Cosmos into their operations for synthetic data generation, while Agility Robotics, Boston Dynamics, and XPENG Robotics are harnessing Isaac's simulation to advance their humanoid efforts.

GE Healthcare is using the new NVIDIA Isaac platform for healthcare simulation built on NVIDIA Omniverse and using NVIDIA Cosmos. The platform speeds development of robotic imaging and surgery systems.

The era of robotics is here, billions of robots, hundreds of millions of autonomous vehicles and hundreds of thousands of robotic factories and warehouses will be developed.

[Frank Sully] NVIDIA's Vision Of AI Factories decrypt.co

Frank Sully — 5/20/2025 7:47:36 PM

[Frank Sully] UAE To Buy Millions of NVIDIA AI GPUs seekingalpha.com

Frank Sully — 5/15/2025 5:39:56 PM

[Frank Sully] NVIDIA & Saudis plan multi-hundred-billion-dollar investments into AI & Robotics...

Frank Sully — 5/15/2025 5:26:42 PM

NVIDIA & Saudis plan multi-hundred-billion-dollar investments into AI & Robotics chips, software & training.

'We thank NVIDIA for their strategic partnership with the Kingdom. This collaboration with HUMAIN marks a turning point, building the AI factories of the future, unlocking compute and powering the next era of physical AI,' said Engineer Abdullah Alswaha, Minister of Communications and Information Technology.

[Frank Sully] Nvidia Is Raising GPU Prices Across Its Entire Lineup Nvidia is reportedly rais...

Frank Sully — 5/12/2025 10:55:06 PM

Nvidia Is Raising GPU Prices Across Its Entire Lineup

Nvidia is reportedly raising its GPU prices across its entire lineup, with customers looking a 10-15% increase as a result of multiple issues the company is facing.

Per Tom's Hardware.

[Frank Sully] What's New In Industrial AI? (3 Minutes) youtu.be

Frank Sully — 5/9/2025 5:42:28 PM

[Frank Sully] Beth Kindig on NVIDIA Nvidia’s Future Hinges on Blackwell – GB200s are the Stan...

Frank Sully — 4/30/2025 12:07:26 AM

Beth Kindig on NVIDIA

Nvidia’s Future Hinges on Blackwell – GB200s are the Standout SKUs

Due to the cyclical nature of GPU shipments, the differences in each generation are critical for investors to track. The Hopper generation has driven immense revenue growth over the past two years, while the Blackwell generation is expected to drive revenue that exceeds 2023 and 2024 combined. Hopper brought Nvidia to a $100 billion data center segment – at $26.3 billion in fiscal Q2 2024 – yet I pointed out how Blackwell could drive the data center segment to $200 billion-plus ten months ago.

[Frank Sully] NVIDIA has many growth areas: The ramping Blackwell chip is sold out through ye...

Frank Sully — 4/14/2025 11:26:28 PM

NVIDIA has many growth areas:

The ramping Blackwell chip is sold out through year-end and is expected to double revenues over last year's Hopper chip.

Coming after Blackwell is the revolutionary Rubin chip, incorporating photonics.

Message 35095854

New Thor chip will revolutionize FSD

Message 35102410

Robotics is NVIDIA's next Gold Mine

seekingalpha.com

[Thomas M.] For the first time, NVIDIA’s AI supercomputers will be built entirely in the U....

Thomas M. — 4/14/2025 10:28:01 PM

For the first time, NVIDIA’s AI supercomputers will be built entirely in the U.S. — working with leading manufacturing partners to design and build factories.

Production has also started for NVIDIA Blackwell chips at TSMC’s chip plants in Arizona.

blogs.nvidia.com

Tom

[Frank Sully] In June, Beth Kindig predicted NVIDIA's market cap would triple to $10T by 2030....

Frank Sully — 12/15/2024 10:56:29 PM

In June, Beth Kindig predicted NVIDIA's market cap would triple to $10T by 2030.

forbes.com

[Frank Sully] Read Beth Kindig! Message 34930390

Frank Sully — 12/15/2024 7:24:35 PM

[Ron] Nvidia’s AI Ascendancy: Q3 Beats Expectations, But Guidance Cools wallstreetpit....

Ron — 11/20/2024 6:35:49 PM

Nvidia’s AI Ascendancy: Q3 Beats Expectations, But Guidance Cools
wallstreetpit.com

[Frank Sully] Q3 & Beyond! NVDA reports Q3 earnings after the bell on November 20th. Q4 guida...

Frank Sully — 11/11/2024 11:14:30 PM

Q3 & Beyond!

NVDA reports Q3 earnings after the bell on November 20th. Q4 guidance will be important to give a hint on the new Blackwell GPU ramp. In CY 2025 Blackwell will reach full production and will drive growth. TSMC has announced that they will double advanced AI chip production in CY 2025, with NVDA getting 50%. Longer term growth will be driven by the advanced Blackwell GPU, followed by the Rubin GPU, and CUDA and NIMs software platforms, including DRIVE autonomous driving, ISAAC and Omniverse Robotics and Digital Twins, LLMs and Digital Avatars and Assistants. Sovereign AI and growth in the Third World, particularly India will contribute. The Global AI Chip and Software markets are projected to grow at a CAGR of 38% for the next five years, growing five-fold. Allowing for some P/E and profit margin compression, I forecast that NVDA's market cap will rise from $3.6 Trillion to $10 Trillion by 2029. To the Moon, Alice!

[Frank Sully] Don't Dig For AI Gold; Sell AI Shovels [graphic]

Frank Sully — 11/11/2024 1:51:56 AM

Don't Dig For AI Gold; Sell AI Shovels

[Frank Sully] NVDA Q2 2025 Earnings Report globalcourant.com

Frank Sully — 8/28/2024 7:06:52 PM

[Ron] Nvidia earnings after the close today: finance.yahoo.com reuters.com

Ron — 8/28/2024 11:14:31 AM

[Frank Sully] NVIDIA Robotics Used By Industry Leaders nvidianews.nvidia.com

Frank Sully — 6/25/2024 6:43:08 PM

[Old Stock Collector] Smart City Expo World Congress Event 2024 Nvidia Dell OneMind in November nvidi...

Old Stock Collector — 6/15/2024 5:00:40 PM

Smart City Expo World Congress Event 2024 Nvidia Dell OneMind in November

nvidia.com

You go to the link, scroll down to Nvidia partners and click on Dell Technologies

Hall 1, C10
Featuring edge Al solutions with NVIDIA Jetson and Metropolis
Discover intelligent video analytics in action including demos in airports by Ipsotek, in stadiums & arenas by IntelexVision, and in public transport by AICUDA and Wobcom. Other demos cover city-as-a-service by Aveya Schneider, IOC & digital twin by Augment City and OneMind, plus Dell's new NativeEdge operations software platform.

These 3 companies will be sharing the same Event Booth!

OneMind Technologies is owned by a tiny 1 cent stock Affluence Corp/ Durham Black Inc ticker AFFU

What is a Tiny nobody company doing with the big boys Nvidia and Dell? This is the 2nd year in a row!

[Zen Dollar Round] Market investors are starting to bet on Nvidia's fall from AI grace Endless gro...

Zen Dollar Round — 6/10/2024 2:25:41 PM

Market investors are starting to bet on Nvidia's fall from AI grace

Endless growth is a mirage, and Jensen Huang won't surf the high heavens for much longer

By Alfonso Maruccia June 10, 2024, 12:57

A hot potato: Nvidia's growth in the past few years has been nothing short of spectacular. The GPU manufacturer is selling a massive number of chips for accelerating AI algorithms, but some investors are now concerned about what comes next. And it's a financial bloodbath.

Nvidia could soon become the most valuable company in the world, but there's growing concern that these stellar market results cannot be sustained for much longer. A recent Reuters report highlighted that short bets against Nvidia have now reached $34 billion, almost twice the amount of bets against Apple ($19 billion) and Tesla ($18 billion).

The Santa Clara-based GPU maker recently achieved a market cap of $3.011 trillion, briefly entering the exclusive "$3 trillion" club along with Apple and Microsoft. Nvidia is expected to eventually overtake Microsoft and become the most valuable company in the world, thanks to the insatiable appetite for AI chips.

The explosive growth of generative AI, chatbots, and other machine learning algorithms has fueled Nvidia's sudden and dramatic increase in market cap. Everyone is investing in some AI-related venture or financial speculation these days, and Nvidia managed to increase its stock value by 143 percent this year alone.

As MarketWatch notes, however, investors in 2023 were already questioning how long Nvidia could maintain the higher-than-average growth levels experienced in recent years. Nvidia's results for the first quarter of fiscal year 2025 were up by 262 percent year-over-year, but those annual increases are expected to slow significantly in the near future.

Some industry watchers have started to sound the alarm about unregulated AI hype, predicting that the AI bubble will soon burst, bringing Nvidia, Microsoft, and many other Big Tech companies back to more realistic expectations about the future of technology. Nothing can grow exponentially forever, professor John Naughton recently told The Guardian, noting that the only planet we live on cannot be " paved with data centers."

AI technology is an environmental disaster in the making, Naughton remarked, and shareholders will soon discover that chatbots are following the same path as previous tech and market disasters like the first internet bubble. Researchers at Capital Economics have even set a date for the inevitable AI bubble burst, predicting 2026 as the year of reckoning after another brief growth period in 2025.

Link: techspot.com

[Ron] Nvidia surpasses Apple to become the second-largest public company in the US ...

Ron — 6/5/2024 6:00:04 PM

Nvidia surpasses Apple to become the second-largest public company in the US
The AI chipmaker’s market capitalization rose to $3.019 trillion on Wednesday, nudging slightly past Apple’s also $2.99 trillion market cap and making it the second-largest publicly traded company in the US by that measure, just behind Microsoft’smarket cap of $3.15 trillion.

Nvidia is now the third company in the US, behind Apple and Microsoft, to cross that $3 trillion mark.

cnn.com

[voop] Reading the conference call transcript, Jensen sees the future and is doing his ...

voop — 5/24/2024 2:25:54 PM

Reading the conference call transcript, Jensen sees the future and is doing his damnest to create it

"
Matthew Ramsay

Thank you very much. Good afternoon, everyone. Jensen, I've been in the data center industry my whole career. I've never seen the velocity that you guys are introducing new platforms at the same combination of the performance jumps that you're getting, I mean, 5x in training. Some of the stuff you talked about at GTC up to 30x in inference. And it's an amazing thing to watch but, it also creates an interesting juxtaposition where the current generation of product that your customers are spending billions of dollars on, it's going to be not as competitive with your new stuff, very, very much more quickly than the depreciation cycle of that product. So I'd like you to -- if you wouldn't mind speak a little bit about how you're seeing that situation evolve itself with customers. As you move to Blackwell, you're going to have very large installed bases, obviously software compatible, but large installed bases of product that's not nearly as performant as your new generation stuff. And it'd be interesting to hear what you see happening with customers along that path. Thank you.

Jensen Huang

Yes. I really appreciate it. Three points that I'd like to make. If you're 5% into the build-out versus if you're 95% into the build out, you're going to feel very differently. And because you're only 5% into the build-out anyhow, you build as fast as you can. And when Blackwell comes, it's going to be terrific. And then after Blackwell, as you mentioned, we have other Blackwells coming. And then there's a short -- we're in a one-year rhythm as we've explained to the world. And we want our customers to see our road map for as far as they like, but they're early in their build-out anyways and so they had to just keep on building, okay. And so there's going to be a whole bunch of chips coming at them, and they just got to keep on building and just, if you will, performance average your way into it. So that's the smart thing to do. They need to make money today. They want to save money today. And time is really, really valuable to them. Let me give you an example of time being really valuable, why this idea of standing up a data center instantaneously is so valuable and getting this thing called time to train is so valuable. The reason for that is because the next company who reaches the next major plateau gets to announce a groundbreaking AI. And the second one after that gets to announce something that's 0.3% better. And so the question is, do you want to be repeatedly the company delivering groundbreaking AI or the company delivering 0.3% better? And that's the reason why this race, as in all technology races, the race is so important. And you're seeing this race across multiple companies because this is so vital to have technology leadership, for companies to trust the leadership and want to build on your platform and know that the platform that they're building on is going to get better and better. And so leadership matters a great deal. Time to train matters a great deal. The difference between time to train that is three months earlier just to get it done, in order to get time to train on three-months project, getting started three months earlier is everything. And so it's the reason why we're standing up Hopper systems like mad right now because the next plateau is just around the corner. And so that's the second reason. The first comment that you made is really a great comment, which is how is it that we're doing -- we're moving so fast and advancing them quickly? Because we have all the stacks here. We literally build the entire data center and we can monitor everything, measure everything, optimize across everything. We know where all the bottlenecks are. We're not guessing about it. We're not putting up PowerPoint slides that look good. We're actually -- we also like our PowerPoint slides look good, but we're delivering systems that perform at scale. And the reason why we know they perform at scale is because we built it all here. Now one of the things that we do that's a bit of a miracle is that we build entire AI infrastructure here, but then we disaggregated and integrated into our customers' data centers however they liked. But we know how it's going to perform and we know where the bottlenecks are. We know where we need to optimize with them and we know where we have to help them improve their infrastructure to achieve the most performance. This deep intimate knowledge at the entire data center scale is fundamentally what sets us apart today. We build every single chip from the ground up. We know exactly how processing is done across the entire system. And so we understand exactly how it's going to perform and how to get the most out of it with every single generation. So I appreciate. Those are the three points.

https://seekingalpha.com/article/4695145-nvidia-corporation-nvda-q1-2025-earnings-call-transcript?source=content_type%3Areact%7Csection%3ATranscripts%7Csection_asset%3ATranscripts%7Cfirst_level_url%3Asymbol%7Cbutton%3ATitle%7Clock_status%3ANo%7Cline%3A1

[Augustus Gloop] This may sound insane but I believe Nvidia has the potential to become the first...

Augustus Gloop — 5/23/2024 4:10:52 PM

This may sound insane but I believe Nvidia has the potential to become the first 5 trillion dollar company. Barring a catastrophic event, I think it's entirely plausible for this company to trade at between $2000 and $2500 per share / by 10 to adjust for the split.

Jensen Huang has become the John Chambers / Michael Dell of conference calls. Lots of sandbagged cash for earnings and now a boatload of shares to buy back to inflate earnings (when necessary).

After 2000 years he has returned.......as a leather wearing Hindu

[Frank Sully] NVIDIA's Blowout Q1 news.alphastreet.com

Frank Sully — 5/23/2024 3:59:35 PM

[Augustus Gloop] Another enormous beat and a 10 for 1 stock split

Augustus Gloop — 5/22/2024 4:48:03 PM

[Julius Wong] Scientists Just Discovered The First-Ever Fractal Molecule In Nature [graphic] ...

Julius Wong — 4/26/2024 10:46:39 PM

Scientists Just Discovered The First-Ever Fractal Molecule In Nature

For the first time, a naturally occurring molecule has been found that exhibits a fractal structure.

Image credit: Max-Planck-Institut for Terrestrial Microbiology/Hochberg

Fractals – those self-similar shapes which can be endlessly zoomed in on without losing detail – are weirdly ubiquitous in nature. You’ve got snowflakes, famously; cauliflowers and coastlines; even rabbits, with a bit of work, can be shown to conform to fractal patterns.

Of course, none of these examples can truly be said to be fractal outside of the math classroom – the real world simply doesn’t work that way. Whilst for mathematicians, zooming into infinity and its reciprocal just requires taking a couple of limits, the rest of us will eventually have to deal with things like “atoms” – and the consequences of trying to go smaller than that.

Which raises a question: how small can we actually go? And according to a recent discovery led by researchers at the Max Planck Institute in Marburg, Germany, the answer is… almost all the way.

“All known regular fractals in nature are made by living organisms and exist at the macroscopic scale. However, none have yet been discovered in nature at the molecular scale,” explains the team in a new paper describing the find.

But that’s all changed now: “We report the discovery of a natural metabolic enzyme capable of forming Sierpinski triangles in dilute aqueous solution at room temperature,” the paper reports.

In other words, they’ve discovered the first-ever naturally occurring fractal molecule.

“We stumbled on this structure completely by accident,” first author Franziska Sendker said in a statement on the find. “[We] almost couldn’t believe what we saw when we first took images of it using an electron microscope.”

“The protein makes these beautiful triangles,” she explained, “and as the fractal grows, we see these larger and larger triangular voids in the middle of them, which is totally unlike any protein assembly we’ve ever seen before.”

So what makes this molecule so different from all the others? With the help of electron microscopy, the team was eventually able to decipher its structure – and what they found defied expectations for how proteins can assemble.

To put it simply, this particular molecule – a citrate synthase from the cyanobacterium Synechococcus elongatus – just isn’t as particular as normal proteins. Instead of building itself up symmetrically, with each individual protein chain being identical in arrangement to its neighbors, S. elongatus assembled itself slightly wonkily. The result? A Sierpinski triangle structure, holding up even as the proteins grow larger.

“This was one of the harder, but also more fascinating structures I have solved in my career,” said Jan Schuller, a researcher at Philipps University Marburg, whose group helped determine the structure.

“The problem with determining the structure of a fractal is that our image averaging techniques kept getting confused by the fact that the smaller triangles can be substructures of larger triangles,” he explained. “The algorithm kept homing in on these smaller triangles instead of seeing the larger structures they were part of.”

And the best part? It seems like this so-far unique molecular structure might have arisen completely by accident.

“[W]e can never be totally sure of the reasons why things happened in the past,” said Georg Hochberg, an evolutionary biologist and senior author of the study. But, he explained, “this particular case does have all the trappings of a seemingly complex biological structure that just popped into existence for no good reason at all because it was simply very easy to evolve.”

Which is exciting, to say the least. Because let’s face it: if molecular-scale Sierpinski triangles have been around us all along, popping up basically randomly just because they could – then what else might be out there, just waiting to be discovered next?

The study is published in the journal Nature.

iflscience.com

[Augustus Gloop] I completely agree. In fact, I agree so much that I've let it take over a much ...

Augustus Gloop — 2/20/2024 8:20:51 AM

I completely agree.

In fact, I agree so much that I've let it take over a much larger % of the portfolio than is acceptable.

[Zen Dollar Round] I suspect NVDA could pull back 10-15% in the short term after earnings are annou...

Zen Dollar Round — 2/20/2024 7:00:58 AM

I suspect NVDA could pull back 10-15% in the short term after earnings are announced tomorrow.

It has run up very fast this year and unless they have a huge blowout to the upside, I can't see how it won't retreat from here.

That said, I'm a huge believer in the company and the stock, with far more upside to come for the next 5-10 years.

[Julius Wong] Nvidia CEO recognized for high-powered GPUs and the AI revolution — Jensen Huang...

Julius Wong — 2/10/2024 7:51:07 AM

Nvidia CEO recognized for high-powered GPUs and the AI revolution — Jensen Huang elected to National Academy of Engineering

tomshardware.com

[Augustus Gloop] I'm getting RSI sickness with NVDA and the entire semi sector....heck, the NASDA...

Augustus Gloop — 1/30/2024 12:52:30 PM

I'm getting RSI sickness with NVDA and the entire semi sector....heck, the NASDAQ in general.

I can't sell anything because the cap gain would be punitive but I'm looking at soxs as a hedge for the short term.

[Glenn Petersen] Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table Chafing at the...

Glenn Petersen — 1/30/2024 5:41:29 AM

Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table

Chafing at their dependence, Amazon, Google, Meta and Microsoft are racing to cut into Nvidia’s dominant share of the market.

By Cade Metz, Karen Weise and Mike Isaac
Reporting from San Francisco and Seattle
New York Times
Jan. 29, 2024, 5:00 a.m. ET

In September, Amazon said it would invest up to $4 billion in Anthropic, a San Francisco start-up working on artificial intelligence.

Soon after, an Amazon executive sent a private message to an executive at another company. He said Anthropic had won the deal because it agreed to build its A.I. using specialized computer chips designed by Amazon.

Amazon, he wrote, wanted to create a viable competitor to the chipmaker Nvidia, a key partner and kingmaker in the all-important field of artificial intelligence.

The boom in generative A.I. over the last year exposed just how dependent big tech companies had become on Nvidia. They cannot build chatbots and other A.I. systems without a special kind of chip that Nvidia has mastered over the past several years. They have spent billions of dollars on Nvidia’s systems, and the chipmaker has not kept up with the demand.

So Amazon and other giants of the industry — including Google, Meta and Microsoft — are building A.I. chips of their own. With these chips, the tech giants could control their own destiny. They could rein in costs, eliminate chip shortages and eventually sell access to their chips to businesses that use their cloud services.

While Nvidia sold 2.5 million chips last year, Google spent $2 billion to $3 billion building about a million of its own A.I. chips, said Pierre Ferragu, an analyst at New Street Research. Amazon spent $200 million on 100,000 chips last year, he estimated. Microsoft said it had begun testing its first A.I. chip.

But this work is a balancing act between competing with Nvidia while working closely with the chipmaker and its increasingly powerful chief executive, Jensen Huang.

Mr. Huang’s company accounts for more than 70 percent of A.I. chip sales, according to the research firm Omdia. It supplies an even larger percentage of the systems used in the creation of generative A.I. Nvidia’s sales have shot up 206 percent over the past year, and the company has added about a trillion dollars in market value.

What’s revenue to Nvidia is a cost for the tech giants. Orders from Microsoft and Meta made up about a quarter of Nvidia’s sales in the past two full quarters, said Gil Luria, an analyst at the investment bank D.A. Davidson.

Nvidia sells its chips for about $15,000 each, while Google spends an average of just $2,000 to $3,000 on each of its own, according to Mr. Ferragu.

“When they encountered a vendor that held them over a barrel, they reacted very strongly,” Mr. Luria said.

Companies constantly court Mr. Huang, jockeying to be at the front of the line for his chips. He regularly appears on event stages with their chief executives, and the companies are quick to say they remain committed to their partnerships with Nvidia. They all plan to keep offering its chips alongside their own.

While the big tech companies are moving into Nvidia’s business, it is moving into theirs. Last year, Nvidia started its own cloud service where businesses can use its chips, and it is funneling chips into a new wave of cloud providers, such as CoreWeave, that compete with the big three: Amazon, Google and Microsoft.

“The tensions here are a thousand times the usual jockeying between customers and suppliers,” said Charles Fitzgerald, a technology consultant and investor.

Nvidia declined to comment.

The A.I. chip market is projected to more than double by 2027, to roughly $140 billion, according to the research firm Gartner. Venerable chipmakers like AMD and Intel are also building specialized A.I. chips, as are start-ups such as Cerebras and SambaNova. But Amazon and other tech giants can do things that smaller competitors cannot.

“In theory, if they can reach a high enough volume and they can get their costs down, these companies should be able to provide something that is even better than Nvidia,” said Naveen Rao, who founded one of the first A.I. chip start-ups and later sold it to Intel.

Nvidia builds what are called graphics processing units, or G.P.U.s, which it originally designed to help render images for video games. But a decade ago, academic researchers realized these chips were also really good at building the systems, called neural networks, that now drive generative A.I.

As this technology took off, Mr. Huang quickly began modifying Nvidia’s chips and related software for A.I., and they became the de facto standard. Most software systems used to train A.I. technologies were tailored to work with Nvidia’s chips.

“Nvidia’s got great chips, and more importantly, they have an incredible ecosystem,” said Dave Brown, who runs Amazon’s chip efforts. That makes getting customers to use a new kind of A.I. chip “very, very challenging,” he said.

Rewriting software code to use a new chip is so difficult and time-consuming, many companies don’t even try, said Mike Schroepfer, an adviser and former chief technology officer at Meta. “The problem with technological development is that so much of it dies before it even gets started,” he said.

Rani Borkar, who oversees Microsoft’s hardware infrastructure, said Microsoft and its peers needed to make it “seamless” for customers to move between chips from different companies.

Amazon, Mr. Brown said, is working to make switching between chips “as simple as it can possibly be.”

Some tech giants have found success making their own chips. Apple designs the silicon in iPhones and Macs, and Amazon has deployed more than two million of its own traditional server chips in its cloud computing data centers. But achievements like these take years of hardware and software development.

Google has the biggest head start in developing A.I. chips. In 2017, it introduced its tensor processing unit, or T.P.U., named after a kind of calculation vital to building artificial intelligence. Google used tens of thousands of T.P.U.s to build A.I. products, including its online chatbot, Google Bard. And other companies have used the chip through Google’s cloud service to build similar technologies, including the high-profile start-up Cohere.

Amazon is now on the second generation of Trainium, its chip for building A.I. systems, and has a second chip made just for serving up A.I. models to customers. In May, Meta announced plans to work on an A.I. chip tailored to its needs, though it is not yet in use. In November, Microsoft announced its first A.I. chip, Maia, which will focus initially on running Microsoft’s own A.I. products.

“If Microsoft builds its own chips, it builds exactly what it needs for the lowest possible cost,” Mr. Luria said.

Nvidia’s rivals have used their investments in high-profile A.I. start-ups to fuel use of their chips. Microsoft has committed $13 billion to OpenAI, the maker of the ChatGPT chatbot, and its Maia chip will serve OpenAI’s technologies to Microsoft’s customers. Like Amazon, Google has invested billions in Anthropic, and it is using Google’s A.I. chips, too.

Anthropic, which has used chips from both Nvidia and Google, is among a handful of companies working to build A.I. using as many specialized chips as they can get their hands on. Amazon said that if companies like Anthropic used Amazon’s chips on an increasingly large scale and even helped design future chips, doing so could reduce the cost and improve the performance of these processors. Anthropic declined to comment.

But none of these companies will overtake Nvidia anytime soon. Its chips may be pricey, but are among the fastest on the market. And the company will continue to improve their speed.

Mr. Rao said his company, Databricks, trained some experimental A.I. systems using Amazon’s A.I. chips, but built its largest and most important systems using Nvidia chips because they provided higher performance and played nicely with a wider range of software.

“We have many years of hard innovation ahead of us,” Amazon’s Mr. Brown said. “Nvidia is not going to be standing still.”
-------------------------------------

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

Karen Weise writes about technology and is based in Seattle. Her coverage focuses on Amazon and Microsoft, two of the most powerful companies in America. More about Karen Weise

Mike Isaac is a technology correspondent for The Times based in San Francisco. He regularly covers Facebook and Silicon Valley. More about Mike Isaac

Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table - The New York Times (nytimes.com)

[Glenn Petersen] Nvidia’s New China Pickle: Customers Don’t Want Its Downgraded Chips Chinese bu...

Glenn Petersen — 1/7/2024 7:01:55 AM

Nvidia’s New China Pickle: Customers Don’t Want Its Downgraded Chips

Chinese buyers are pushing back against the lower-powered AI chips it hopes to sell to them in response to U.S. export curbs

By Raffaele Huang
Bloomberg
Jan. 6, 2024 11:00 pm ET

Nvidia, based in Santa Clara, Calif., is trying to meet Chinese clients’ demands while complying with U.S. export rules. PHOTO: PHILIP PACHECO/BLOOMBERG NEWS
------------------------

SINGAPORE—After U.S. regulations barred Nvidia from selling its high-performance artificial-intelligence chips to China in October, the company’s engineers quickly designed a new lineup to comply with the tightened rules.

The U.S. tech company may have found some wiggle room, but it faces a bigger problem: Chinese cloud companies—some of Nvidia’s biggest customers globally—aren’t so keen on buying its lower-powered AI chips.

Alibaba Group and Tencent are among China’s biggest cloud companies that have been testing Nvidia samples since November. They have indicated to Nvidia that they would order far fewer chips from the company this year than they had originally planned to buy of its now-banned previous chips, people familiar with the matter said.

In the short term, Nvidia’s downgrading of its processors is narrowing the performance gap with local alternatives, making China-made chips increasingly attractive to buyers.
Alibaba and Tencent are shifting some advanced semiconductor orders to homegrown companies such as Huawei Technologies and relying more on chips they can develop in-house, the people said. So too are China’s other top two buyers—AI pioneer Baidu and TikTok owner ByteDance.

In the longer term, Chinese buyers are uncertain about Nvidia’s ability to continue to supply them, given U.S. regulators have pledged to review the chip-export controls regularly and could further tighten performance limits, the people said. Tech firms are modifying their business strategies to prepare for a future with less access to Nvidia’s products, and to avoid the costly process of constantly adjusting their technology to adapt to new chips.
For Santa Clara, Calif.-based Nvidia, threading the needle between the demands of U.S. regulators and supplying chips that Chinese clients need is getting more difficult. The company has billions of dollars of unfulfilled orders for its earlier chips, and China is one of its biggest markets, historically contributing about a fifth of its revenue.

Nvidia CEO Jensen Huang has acknowledged Huawei as a ‘formidable competitor’ to the company in China. PHOTO: WALID BERRAZEG/ZUMA PRESS
----------------------------

Nvidia’s chips are currently in higher demand than it can meet. Nonetheless, geopolitical tensions pose the longer-term risk of lost sales in the world’s second-biggest economy, which is pursuing AI development as a strategic priority.

Chinese cloud companies currently source around 80% of high-end AI chips from Nvidia, and that is likely to decline to 50%-60% in the next five years, said Frank Kung, an analyst at tech-research firm TrendForce. He added that tightening U.S. chip controls in the future would create additional pressure on Nvidia’s China sales.

Nvidia said it is working to offer products that comply with U.S. rules to customers worldwide.

Nvidia has said it doesn’t see a short-term financial impact from restrictions on shipment of its AI chips to China because it can find other buyers for them. But Chief Financial Officer Colette Kress said last year that in the long run, prohibiting the sale of AI chips in China would keep the U.S. industry from being able to compete and lead in one of the world’s largest markets.

Switching to Huawei Chips

Over the past two years, the Biden administration has imposed two rounds of export sanctions to curtail China’s access to advanced chips and technology that the U.S. says Beijing could use to advance its military and surveillance capabilities. Nvidia Chief Executive Jensen Huang has said he still hopes to supply high-end processors to China and is working with customers in China to get export licenses.

After the first curbs in October 2022, Nvidia modified the chips it sold in China by scaling back their performance to fall below the thresholds that would require U.S. government oversight. It sold more than $1 billion worth of such chips to Chinese customers in 2023.

When the U.S. a year later cut off more Nvidia chip exports to China without a license, the chip maker developed another new lineup of less powerful processors for Chinese buyers, which it plans to release early this year, The Wall Street Journal reported. Last month, Nvidia launched the GeForce RTX 4090 D, a modified version of its top-of-the-line gaming chip adapted after the latest U.S. curbs.

Commerce Secretary Gina Raimondo met in August with Chinese Premier Li Qiang during a trip in which export controls were high on her agenda. PHOTO: RAO AIMIN/ZUMA PRESS
-------------------------------

Chinese companies have been testing samples of Nvidia’s new highest-performing AI chip in the coming lineup, called the H20. The chip allows data to transmit among multiple processors efficiently, making it a better option than homegrown alternatives for building chip clusters needed to handle the computational workloads of AI, some testers said.

Still, testers said they needed more of the H20s to simulate the computing power that they got from Nvidia’s previous chips, increasing their costs.

The most advanced Chinese chips are as capable of handling inference—in which a trained AI model comes up with predictions—and less complex training tasks as are U.S. chips, developers say.
Huawei, acknowledged by Nvidia’s Huang to be a “formidable competitor” to the firm in China, is gaining ground at the expense of the U.S. chip maker.

In 2023, Huawei received orders for at least 5,000 Ascend 910B chips from major Chinese internet companies, people familiar with the matter said. The chip is considered the closest available Chinese alternative to Nvidia’s export-barred high-performance A100.

The chips are meant to be delivered through 2024, they said, as Huawei faces production constraints because of U.S. sanctions.

Chinese official procurements, such as those from state-owned telecom operators, have called for the adoption of homegrown chips such as Huawei’s. China Telecom acquired about $390 million worth of AI servers powered by Huawei chips in October, while China Unicom spent at least $20 million in 2022, according to company purchasing documents.

Huawei has stepped up efforts to expand its software ecosystem and plans to launch a new high-end AI chip as soon as the second half of 2024, the people said.

A few government-backed AI computing centers have sourced Huawei’s chips since the U.S. imposed curbs in 2022.

AI App Maker Skips Nvidia Chips

Alibaba’s chip arm, T-Head, is also developing a new specific-purpose AI processor under its Hanguang label, people familiar with the matter said.

“If the restrictions are likely to only get tighter in the next few years, you’d better start thinking about alternatives now,” said a senior executive at Alibaba Cloud.

The frenzy over generative AI earlier last year spurred demand for Nvidia’s advanced chips as major Chinese companies and startups tried to develop their own large language models. Now, many smaller players are scaling back such efforts and shifting to focus on AI applications.

Kenneth Yang, a Shanghai-based co-founder of a healthcare AI startup, said he plans to skip Nvidia’s latest China-focused chips and lease AI processing power from Baidu or Huawei instead.

“It’s about spending money in a smart way,” said Yang, who is developing a nursing-care app.

Engineers at Chinese technology companies say Nvidia’s chips will remain a purchasing priority over the next 12 months, given Nvidia’s more extensive product ecosystem and as local alternatives continue to be in short supply.

In the longer run, U.S. curbs are likely to push the Chinese to develop their own technologies, said Kevin Xu, founder of hedge fund Interconnected Capital, who authors a newsletter about tech and geopolitics.
“After this current phase of stockpiling is done, Nvidia’s China business will become a sacrificial lamb,” he said.

—Asa Fitch and Yuka Hayashi contributed to this article.

Write to Raffaele Huang at raffaele.huang@wsj.com

Nvidia’s New China Pickle: Customers Don’t Want Its Downgraded Chips - WSJ (archive.ph)

[Glenn Petersen] Nvidia to launch slower version of its gaming chip in China to comply with U.S. ...

Glenn Petersen — 12/29/2023 6:37:31 AM

Nvidia to launch slower version of its gaming chip in China to comply with U.S. export controls

PUBLISHED FRI, DEC 29 20235:09 AM EST
UPDATED AN HOUR AGO
Ryan Browne @RYAN_BROWNE_
CNBC.com

KEY POINTS

U.S. chipmaking giant Nvidia is set to launch an adjusted version of a gaming processor with slower performance in China to comply with U.S. export restrictions.
A product page on Nvidia’s website for Chinese consumers shows that the new Nvidia RTX 4090D has 11% fewer processing cores than versions sold outside of China.
A Nvidia spokesperson told Reuters that the chip was “designed to fully comply with U.S. government export controls.”

U.S. chipmaking giant Nvidia is set to launch an adjusted version of a gaming processor with slower performance in China to comply with U.S. export restrictions.

A product page on Nvidia’s website for Chinese consumers shows that the new Nvidia RTX 4090D — which a spokesperson says will be launched in January, according to Reuters — has 11% fewer “CUDA” (Compute Unified Device Architecture) cores than versions of the chip that are sold outside of China.

RTX is Nvidia’s line of advanced gaming GPUs, or graphics processing units. The company’s CUDA architecture is essentially a GPU equivalent for CPU cores, which are processing units.

Nvidia was not immediately available for comment when contacted by CNBC, but a company spokesperson told Reuters that the chip has been “designed to fully comply with U.S. government export controls.”

Nvidia “extensively engaged with the U.S. government” while developing the product, the spokesperson said, according to Reuters.

Washington has imposed export restrictions on China that prevent companies in the country from accessing some of the most advanced chips from American firms.

The export rules primarily target chips that enable AI applications, but gaming-focused processors are also in the firing line as many also have potential uses in artificial intelligence.

The Nvidia RTX 4090 was included on the list of banned U.S.-made chips, according to an Oct. 17 Securities and Exchange Commission filing. On its website, Nvidia says the RTX 4090D chip leverages AI to enhance performance.

Shares of Nvidia have surged in 2023, more than tripling in price year-to-date. The company has benefited from a rush of demand for AI, thanks in no small part to the immense buzz caused by OpenAI’s ChatGPT chatbot.

Nvidia brings slower gaming chip version to China to bypass U.S. rules (cnbc.com)

[Frank Sully] Here's why NVIDIA Stock could double in 2024 fool.com

Frank Sully — 12/23/2023 3:33:23 PM

[Frank Sully] AMD's Answer to NVIDIA? - 30 Minure Excerpt from last week's presentation compar...

Frank Sully — 12/12/2023 9:20:07 PM

AMD's Answer to NVIDIA? - 30 Minure Excerpt from last week's presentation comparing AMD's MI300X TO NVIDIA's H100. Note that they don't compare to new NVIDIA H200 super hip.

youtu.be

[Glenn Petersen] He Built a Trillion-Dollar Company. He Wouldn’t Do It Again. Jensen Huang, th...

Glenn Petersen — 12/9/2023 6:22:23 AM

He Built a Trillion-Dollar Company. He Wouldn’t Do It Again.

Jensen Huang, the CEO of the year’s most successful company, has a theory about the superpower of entrepreneurs

By Ben Cohen
Wall Street Journal
Updated Dec. 9, 2023 12:16 am ET

When he sat down in a booth at his local Denny’s and began plotting out the business that would change his life, Jensen Huang didn’t know that his startup would one day be worth $1 trillion. In fact, the only chief executive in Nvidia’s history didn’t know much of anything about what he was getting himself into.

But if he had known three decades ago what he knows today, he never would have founded one of the world’s most valuable companies.

“The reason for that is really quite simple,” Huang said recently. “Building Nvidia turned out to have been a million times harder than I expected.”

Nvidia was the stock market’s big winner of 2023, when the chip maker cracked $1 trillion in value. That would have seemed impossible 30 years ago, and it wasn’t especially probable just one year ago, before the AI boom made Nvidia worth more than Netflix, Nike and Novo Nordisk combined.

So why wouldn’t he do it again?

“If we realized the pain and suffering and how vulnerable you’re going to feel, the challenges that you’re going to endure, the embarrassment and the shame and the list of all the things that go wrong,” he said, “nobody in their right mind would do it.”

The candor from one of tech’s longest-tenured CEOs wasn’t just eye-opening. Huang’s comments were also a rare peek into the mind of one of the most successful entrepreneurs of his generation, someone who took an idea hatched over Grand Slam breakfasts and Super Bird turkey sandwiches and turned it into a trillion-dollar company. Along the way he learned an important, counterintuitive lesson.

Everyone in Silicon Valley knows they have to be resilient. Huang knows it also helps to be ignorant.

“I think that’s kind of the superpower of an entrepreneur,” he said. “They don’t know how hard it is. And they only ask themselves: How hard can it be? To this day, I trick my brain into thinking: How hard can it be?”

Really hard, as it turns out. He didn’t know that the original business plan had no chance of success. He didn’t know how many times he would fail. And he didn’t know just how much he didn’t know. But just because the 60-year-old billionaire says he wouldn’t do it again doesn’t mean he’s telling other people they shouldn’t. In fact, the opposite: Only they have the advantage of being undaunted by the difficulty of building a company.

Jensen Huang has been running Nvidia since his silvery hair was the color of his signature black leather jacket. PHOTO: I-HWA CHENG/AGENCE FRANCE-PRESSE/GETTY IMAGES
----------------------------------

Huang made his comments in a recent interview with Acquired, a tech podcast hosted by Ben Gilbert and David Rosenthal, who might know more about Nvidia’s history than anyone who didn’t live through it. After releasing three deeply researched, delightfully wonky episodes about the company’s strategy, the podcasters were invited to Nvidia headquarters for an interview with the CEO himself. (Huang declined to comment for this article.)

Huang has been running the company since his silvery hair was the color of his signature black leather jacket. Even after three decades on the job, Huang remains actively involved at Nvidia. He still manages 50 senior executives who report directly to him and attends product meetings with junior employees who weren’t alive when the company was born. There has never been a business worth so much that people know so little about. But the more the podcasters studied Nvidia’s success, the more they credited one person.

“That company is him,” Rosenthal told me. “He does everything but sweep the floors—and he may sweep the floors.”

So when it came time for one final question, they were curious: If he were 30 years old today, sitting in that Denny’s again, what kind of company would he be starting?

He said he wouldn’t start one at all. He might as well have said Nvidia’s chips were made of Doritos.

But his response began to make sense when he reflected on the wrenching years before this year. There are only five American companies worth at least $1 trillion right now. Apple, Microsoft and Alphabet’s stock prices have never dropped 85% from high to low. Amazon had one such drawdown. Nvidia survived two.

Those excruciating stretches in 2002 and 2008 now look so insignificant that you can barely see them on Nvidia’s historical stock chart. They didn’t feel that way at the time. And he got an unwelcome reminder of that feeling when the company lost half its value last year.

Huang circa 2003, not long after Nvidia survived a near-death experience during the dot-com bust. PHOTO: MEDIANEWS GROUP VIA GETTY IMAGES
------------------------------------

But after sputtering in 2022, Nvidia exploded in 2023. That’s because there has never been so much demand for GPUs, the advanced chips that provide oxygen for artificial intelligence, powering almost every piece of technology the nerdiest person you know is psyched about, and Huang’s company controls the supply. AI models require tens of thousands of these graphics-processing units that can handle lots of computational tasks at the same time, and they’re made almost entirely by Nvidia because Huang invested in GPUs long before there was a roaring market for them.

Nvidia’s central role in the AI economy is the reason it has tripled in value and beat every other company in the S&P 500 this year. It’s on pace for the best annual performance of any major stock in the past decade.

Which made the recent comments from one of the world’s richest men all the more curious.

Huang had a better year than anyone this side of Taylor Swift. But even at the height of his company’s success, he remains haunted by the prospect of failure. According to the New Yorker, Nvidia’s unofficial motto is his mantra from the startup’s early, uncertain years: “Our company is 30 days from going out of business.” At this point, Nvidia is worth more than the other American chip giants put together, and AI would have to destroy the world for Huang’s company to be out of business in a month. But he’s still driven by that fear.

“You’re always on the way to going out of business,” he recently said at Columbia Business School. “If you don’t internalize that sensibility, you will go out of business.”

The moments when his company nearly crashed are burned into Huang’s memory as permanently as the Nvidia logo tattooed on his arm.

When the world’s most valuable chip maker was founded in 1993 by Huang, Chris Malachowsky and Curtis Priem, the only people paying attention to them were the waiters of a Denny’s in San Jose, Calif. There was no reason to suspect three lousy customers guzzling too much coffee were laying the foundation of a revolutionary company. And when Huang told people he was making graphics cards for videogames, his own mother told him to get a real job.

But the secret to Nvidia’s early success wasn’t the people involved or the industry they set out to conquer. It was the unusual, informal governance structure they chose for their startup.

Huang was always in charge, and Malachowsky and Priem reported to him, but they made a deal that each founder would have authority in his own fiefdom.

“We would talk or argue over each other’s decisions, but we would default to the final decision of the person who had the expertise in that area,” Priem told me. “It wasn’t ‘agree to disagree.’ The decision terminated any disagreements and became the direction we were going.”

Nvidia’s chips are indispensable for the AI boom. PHOTO: MARLENA SLOSS/BLOOMBERG NEWS
-----------------------------------

Their arrangement made Huang responsible for business operations and finding partners to manufacture its chips. But that was a huge burden for one person, which Priem learned the one time he told Huang what he thought he should do. “He unloaded on me,” said Priem, “telling me all the responsibilities he had and all the things he was juggling.” He was stunned: Huang had kept the pressures of his job to himself. “It was an oh-my-God moment for me to understand how alone he was in his role,” Priem told me.

A trillion dollars in market value hasn’t made Huang’s job any easier. These days, his company must navigate tight U.S. regulations meant to stifle China’s access to powerful chips, not to mention increased competition from rivals at home desperate to pierce Nvidia’s dominance.

But it was much harder when Nvidia wasn’t as successful.

After the company released its first product, a graphics card that flopped, Huang laid off half the workforce. Running out of money, teetering on the edge of bankruptcy, he bet the company on the 1997 chip that saved Nvidia. But the decade after Huang’s company went public in 1999 would bring two more brutal stretches during the dot-com bust and global financial crisis. Even when markets rallied, Nvidia didn’t. From 2008 to 2013, when the S&P 500 was up 25%, Nvidia was down 50%.

The whole company was worth less than the $6 billion that Huang personally made in a single day of trading this year.

Nvidia stagnated as Huang plowed money into a new platform for accelerated computing, one that would allow developers to do anything they wanted with GPUs. Wall Street was skeptical of his vision of the future. But there was one group of people who could see it: AI researchers. Once they began using Nvidia’s chips to train neural networks, they realized the transformational potential of Huang’s tools.

And then he decided to put his chips on the table again.

The initial breakthroughs in deep learning compelled Huang to make another bet-the-company move on AI. Nvidia began work in 2012 on the system that would become its first AI supercomputer. Huang delivered it four years later to OpenAI, whose researchers would use Nvidia’s GPUs to educate ChatGPT, which became the hottest app in tech history when it was released last year.

But this was the year those chips became the picks and shovels of a gold rush.

Now there are young entrepreneurs sitting in their own metaphorical Denny’s, dreaming about building companies and completely unaware of how hard it’s going to be.

Despite Nvidia’s success, Huang says he wouldn’t have started the company if he had realized how difficult it would be. PHOTO: I-HWA CHENG/BLOOMBERG NEWS
-------------------------------

Because how hard could it be?

He Built a Trillion-Dollar Company. He Wouldn’t Do It Again. - WSJ

[Glenn Petersen] Meta and Microsoft say they will buy AMD’s new AI chip as an alternative to Nvid...

Glenn Petersen — 12/7/2023 6:15:30 AM

Meta and Microsoft say they will buy AMD’s new AI chip as an alternative to Nvidia’s

PUBLISHED WED, DEC 6 20234:09 PM ESTUPDATED AN HOUR AGO
Kif Leswing @KIFLESWING
CNBC.com

KEY POINTS

Meta, OpenAI, and Microsoft said they will use AMD’s newest AI chip, the Instinct MI300X — a sign that tech companies want alternatives to the expensive Nvidia graphics processors that have been essential for artificial intelligence.
If the MI300X is good enough and inexpensive enough when it starts shipping early next year, it could lower costs for developing AI models.
AMD CEO Lisa Su projected the market for AI chips will amount to $400 billion or more in 2027, and she said she hopes AMD has a sizable part of that market.

Lisa Su displays an AMD Instinct MI300 chip as she delivers a keynote address at CES 2023 in Las Vegas, Nevada, Jan. 4, 2023.
David Becker | Getty Images
--------------------------------
Meta, OpenAI, and Microsoft said at an AMD investor event Wednesday they will use AMD’s newest AI chip, the Instinct MI300X. It’s the biggest sign so far that technology companies are searching for alternatives to the expensive Nvidia graphics processors that have been essential for creating and deploying artificial intelligence programs such as OpenAI’s ChatGPT.

If AMD’s latest high-end chip is good enough for the technology companies and cloud service providers building and serving AI models when it starts shipping early next year, it could lower costs for developing AI models and put competitive pressure on Nvidia’s surging AI chip sales growth.

“All of the interest is in big iron and big GPUs for the cloud,” AMD CEO Lisa Su said Wednesday.

AMD says the MI300X is based on a new architecture, which often leads to significant performance gains. Its most distinctive feature is that it has 192GB of a cutting-edge, high-performance type of memory known as HBM3, which transfers data faster and can fit larger AI models.

Su directly compared the MI300X and the systems built with it to Nvidia’s main AI GPU, the H100.

“What this performance does is it just directly translates into a better user experience,” Su said. “When you ask a model something, you’d like it to come back faster, especially as responses get more complicated.”

The main question facing AMD is whether companies that have been building on Nvidia will invest the time and money to add another GPU supplier. “It takes work to adopt AMD,” Su said.

AMD on Wednesday told investors and partners that it had improved its software suite called ROCm to compete with Nvidia’s industry standard CUDA software, addressing a key shortcoming that had been one of the primary reasons AI developers currently prefer Nvidia.

Price will also be important. AMD didn’t reveal pricing for the MI300X on Wednesday, but Nvidia’s can cost around $40,000 for one chip, and Su told reporters that AMD’s chip would have to cost less to purchase and operate than Nvidia’s in order to persuade customers to buy it.

Who says they’ll use the MI300X?

AMD MI300X accelerator for artificial intelligence.
--------------------------------

On Wednesday, AMD said it had already signed up some of the companies most hungry for GPUs to use the chip. Meta and Microsoft were the two largest purchasers of Nvidia H100 GPUs in 2023, according to a recent report from research firm Omidia.

Meta said it will use MI300X GPUs for AI inference workloads such as processing AI stickers, image editing, and operating its assistant.

Microsoft’s CTO, Kevin Scott, said the company would offer access to MI300X chips through its Azure web service.

Oracle’s cloud will also use the chips.

OpenAI said it would support AMD GPUs in one of its software products, called Triton, which isn’t a big large language model like GPT but is used in AI research to access chip features.

AMD isn’t forecasting massive sales for the chip yet, only projecting about $2 billion in total data center GPU revenue in 2024. Nvidia reported more than $14 billion in data center sales in the most recent quarter alone, although that metric includes chips other than GPUs.

However, AMD says the total market for AI GPUs could climb to $400 billion over the next four years, doubling the company’s previous projection. This shows how high expectations are and how coveted high-end AI chips have become — and why the company is now focusing investor attention on the product line.

Su also suggested to reporters that AMD doesn’t think that it needs to beat Nvidia to do well in the market.

“I think it’s clear to say that Nvidia has to be the vast majority of that right now,” Su told reporters, referring to the AI chip market. “We believe it could be $400 billion-plus in 2027. And we could get a nice piece of that.”

Meta and Microsoft to buy AMD's new AI chip as alternative to Nvidia (cnbc.com)

[Frank Sully] NVIDIA: AI NEWS AND FORECAST fool.com

Frank Sully — 12/5/2023 8:55:32 PM

[Selectric II] What percentage of their shares does that represent? <10%? "... Mark Stevens, ...

Selectric II — 12/5/2023 3:28:28 PM

What percentage of their shares does that represent? <10%?

"... Mark Stevens, a director at Nvidia since 2008. The former managing partner at venture capital firm Sequoia Capital filed a form 144 on Nov. 24 indicating the planned sale of 300,000 shares and sold 10,280 shares on Nov. 24 and Nov. 27. Stevens has sold hundreds of thousands of shares over the past few years and still has a stake in Nvidia worth about $2 billion, according to data compiled by Bloomberg...."

That tells me just about all I need to know.

[Julius Wong] U.S. issues warning to NVIDIA, urging to stop redesigning chips for China video...

Julius Wong — 12/4/2023 11:46:47 AM

U.S. issues warning to NVIDIA, urging to stop redesigning chips for China

videocardz.com

[Julius Wong] Nvidia Insiders Unload Shares After 220% AI Rally Nvidia Corp. executives and d...

Julius Wong — 12/4/2023 11:13:19 AM

Nvidia Insiders Unload Shares After 220% AI Rally

Nvidia Corp. executives and directors last month sold or filed paperwork showing they intend to sell roughly 370,000 shares worth about $180 million, according to data compiled by the Washington Service.

finance.yahoo.com

[Frank Sully] China is using local AI chips in lieu of NVIDIA asiatimes.com

Frank Sully — 12/1/2023 7:08:43 PM

[Frank Sully] Please explain why Augustus.

Frank Sully — 12/1/2023 7:06:30 PM

[Augustus Gloop] Yesterday was mind bending

Augustus Gloop — 11/30/2023 12:00:06 PM

[Glenn Petersen] Why Amazon and Nvidia Need Each Other Amazon’s in-house chip program can’t full...

Glenn Petersen — 11/29/2023 10:21:01 AM

Why Amazon and Nvidia Need Each Other

Amazon’s in-house chip program can’t fully match Nvidia’s AI dominance, while chip maker needs to keep its U.S. business strong

By Dan Gallagher
Heard on the Street
Wall Street Journal
Nov. 29, 2023 6:00 am ET

Nvidia CEO Jensen Huang, right, joined AWS CEO Adam Selipsky at the annual AWS re:Invent conference on Tuesday. PHOTO: AMAZON
----------------------------
Amazon AMZN needed to put on a good AI show this week. It got a little help from a surprising friend.
The e-commerce titan also happens to run the world’s largest cloud-computing business. In fact, Amazon’s AWS unit now generates significantly more annual revenue than IBM and Oracle and comes second only to
Microsoft in the market for business-focused software and related services. But Amazon has also been perceived as lagging behind its largest cloud rival in the field of generative artificial intelligence, given Microsoft’s aggressive push into the technology since the public launch of ChatGPT almost exactly one year ago.

Hence, Amazon used its annual AWS re:Invent conference on Tuesday to lean hard into generative AI. It even announced its own chatbot called Q, which looks like a business-focused version of Microsoft’s Copilot. But most notable was the appearance of Nvidia NVDA Chief Executive Officer Jensen Huang, who joined AWS CEO Adam Selipsky on stage at the Las Vegas event to announce an “expanded collaboration” between the two companies. That will include AWS being the first cloud provider to launch services with Nvidia’s new H200 “superchips” that will start shipping next year.

Tech executives often cross-pollinate each other’s trade shows, and the occasions are generally not worthy of note. But this was the first time Huang has appeared at the annual confab for AWS, which has been a major customer of Nvidia’s data-center business over the last several years.

And it came amid rumors of growing friction between the two companies, as Amazon has gone further than its cloud rivals in designing its own in-house chips, while Nvidia has been pushing into offering cloud-computing services of its own. Amazon even used the same keynote on Tuesday to announce the fourth version of its Graviton processor and the second version of its Trainium accelerator—the latter of which competes with Nvidia’s chips in the training of AI models.

Amazon CEO Andy Jassy bragged on the company’s third-quarter call last month that its Trainium chips “have better price performance characteristics than the other options out there, but also the fact that you can get access to them”—the last part a dig at the well-known shortage of Nvidia’s very in-demand chips. That fed further the view that Amazon might have been on the outs with the supplier of a vital AI component. Nvidia mentioned Microsoft 10 times in its own earnings call last week compared with just one mention of Amazon.

Yet the truth is that the two companies very much need each other. Nvidia’s early moves in artificial intelligence have given the company a strong position that can’t be fully replicated even by in-house chips from established tech giants, who can custom design silicon for their own networks. That has been readily apparent in Nvidia’s recent financial results; the chip maker’s data-center sales have quadrupled over the past two quarters compared with the same period last year. Nvidia credited cloud-service providers as accounting for about half those sales.

But Nvidia also can’t afford to alienate the biggest buyer in the market. Amazon’s total annual capital expenditures have been more than twice that of Microsoft’s over the past four years, and market-research firm Dell’Oro Group estimates that the data-center portion of Amazon’s capex totaled $29 billion last year—39% above Microsoft’s estimated spend for the year. Nvidia is also facing the prospect that its sales to China could take a serious hit because of new export controls, making a strong relationship with a huge U.S. customer even more important. Friends at home count for a lot these days.

Write to Dan Gallagher at dan.gallagher@wsj.com

Why Amazon and Nvidia Need Each Other - WSJ (archive.ph)

[Frank Sully] NVIDIA Taps China Talent For Autonomous Driving Staff techcrunch.com

Frank Sully — 11/28/2023 12:18:07 PM