Posted on 07/13/2025 5:14:11 PM PDT by nwrep
The top CEO in the world, Nvidia's Jensen Huang
Nvidia NVDA +0.50% ▲ CEO Jensen Huang has reached Warren Buffett in net worth, a major milestone that has been driven by Nvidia’s booming success in the AI chip space. According to Fortune, Huang’s wealth reached $143.7 billion on Friday morning, which puts him just ahead of Buffett’s $142.1 billion. At the same time, Bloomberg’s Billionaires Index showed that the two are nearly tied, with Huang at $143 billion and Buffett at $144 billion. Regardless of the exact numbers, Huang’s fortune has grown by about $29 billion so far in 2025.
Regardless of the exact numbers, Huang’s fortune has grown by about $29 billion so far in 2025.
(Excerpt) Read more at tipranks.com ...
What do Omar, Crockett, Ocasio-Cortez, Sanders, et al have to say about this “greedy oligarch”?
Huang has created probably ten million more jobs than all those commies put together.
Pelosi has gotten so rich off this guy.
At least he created something. Pelosi, Omar, Crockett, Ocasio-Cortez, Sanders, have never created anything...
He and the other two started the company off with $40,000 and used a townhouse owned by one of them for an office.
How long until some judgie poo says he’s paid too much and takes back money from his pay ?
I would rather have Buffet’s portfolio.
We're blessed by men like Jensen Huang - men who follow their dreams, take risks and create businesses so all of us can live better lives. Capitalism works.
“ Capitalism works”
The proof is all around us in so many ways every single day. But the brain-dead idiots who adore communism never once ask the obvious question: “Why did NONE of these wonders originate in the Soviet Union, any Soviet “Socialist Republic,” in North Korea, in Cuba, in Venezuela?”
I didn’t read the article, but I saw an article title proclaiming AMD could make Nvidia performance level chips.
Note: The claim is beyond my ability to verify.
Nvidia and its chips have always been well-respected by me.
However, $4 trillion is really big money.
“Huang has created probably ten million more jobs than all those commies put together.”
He has created a lot of wealth and a lot of jobs, but I doubt even one million jobs.
There is going to be a lot of money made from AI, such as from education, policing and elder care, but the software development may not be fast enough to see it mostly in the form of AI chip sales profits.
WIKI
Nvidia Corporation[a] (/ɛnˈvɪdiə/ en-VID-ee-ə) is an American multinational corporation and technology company headquartered in Santa Clara, California, and incorporated in Delaware. Founded in 1993 by Jensen Huang (president and CEO), Chris Malachowsky, and Curtis Priem, it designs and supplies graphics processing units (GPUs), application programming interfaces (APIs) for data science and high-performance computing, and system on a chip units (SoCs) for mobile computing and the automotive market. The company is also a leading supplier of artificial intelligence (AI) hardware and software. Nvidia outsources the manufacturing of the hardware it designs.
Its GeForce line of GPUs is aimed at the consumer market and is used in applications such as video editing, 3D rendering, and PC gaming. With a market share of 80.2% in the second quarter of 2023, Nvidia leads global sales of discrete desktop GPUs by a wide margin.
Nvidia was founded on April 5, 1993, by Jensen Huang (who remains CEO), a Taiwanese-American electrical engineer who was previously the director of CoreWare at LSI Logic and a microprocessor designer at AMD; Chris Malachowsky, an engineer who worked at Sun Microsystems; and Curtis Priem, who was previously a senior staff engineer and graphics chip designer at IBM and Sun Microsystems. In late 1992, the three men agreed to start the company in a meeting at a Denny’s roadside diner on Berryessa Road in East San Jose.
The three co-founders envisioned graphics-based processing as the best trajectory for tackling challenges that had eluded general-purpose computing methods. As Huang later explained: “We also observed that video games were simultaneously one of the most computationally challenging problems and would have incredibly high sales volume. Those two conditions don’t happen very often. Video games was our killer app — a flywheel to reach large markets funding huge R&D to solve massive computational problems.
During the late 1990s, Nvidia was one of 70 start-up companies pursuing the idea that graphics acceleration for video games was the path to the future. Only two survived: Nvidia and ATI Technologies, the latter of which merged into AMD.
In a pivotal moment, Sega’s president, Shoichiro Irimajiri, visited Huang in person to inform him that Sega had decided to choose another vendor for the Dreamcast. However, Irimajiri believed in Nvidia’s potential and persuaded Sega’s management to invest $5 million into the company. Huang later reflected that this funding was all that kept Nvidia afloat, and that Irimajiri’s “understanding and generosity gave us six months to live”.
In 1996, Huang laid off more than half of Nvidia’s employees—thereby reducing headcount from 100 to 40—and focused the company’s remaining resources on developing a graphics accelerator product optimized for processing triangle primitives: the RIVA 128. By the time the RIVA 128 was released in August 1997, Nvidia had only enough money left for one month’s payroll. The sense of impending failure became so pervasive that it gave rise to Nvidia’s unofficial company motto: “Our company is thirty days from going out of business.
In late 1999, Nvidia released the GeForce 256 (NV10), its first product expressly marketed as a GPU, which was most notable for introducing onboard transformation and lighting (T&L) to consumer-level 3D hardware. Running at 120 MHz and featuring four-pixel pipelines, it implemented advanced video acceleration, motion compensation, and hardware sub-picture alpha blending. The GeForce outperformed existing products by a wide margin.
Due to the success of its products, Nvidia won the contract to develop the graphics hardware for Microsoft’s Xbox game console, which earned Nvidia a $200 million advance.
I can’t understandably describe the more recent history:
https://en.wikipedia.org/wiki/Nvidia
WIKI
The Hopper architecture was the first Nvidia architecture to implement the transformer engine. The transformer engine accelerates computations by dynamically reducing them from higher numerical precisions (i.e., FP16) to lower precisions that are faster to perform (i.e., FP8) when the loss in precision is deemed acceptable. The transformer engine is also capable of dynamically allocating bits in the chosen precision to either the mantissa or exponent at runtime to maximize precision.
In late 2022, due to US regulations limiting the export of chips to the People’s Republic of China, Nvidia adapted the H100 chip to the Chinese market with the H800. This model has lower bandwidth compared to the original H100 model. In late 2023, the US government announced new restrictions on the export of AI chips to China, including the A800 and H800 models. This led to Nvidia creating another chip predicated on Hopper microarchitecture: the H20, a modified version of the H100. The H20 had become the most prominent chip in the Chinese market as of 2025.
https://en.wikipedia.org/wiki/Hopper_(microarchitecture)
WIKI
The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.
Smith–Waterman algorithm aligns two sequences by matches/mismatches (also known as substitutions), insertions, and deletions. Both insertions and deletions are the operations that introduce gaps, which are represented by dashes. The Smith–Waterman algorithm has several steps:
Determine the substitution matrix and the gap penalty scheme. A substitution matrix assigns each pair of bases or amino acids a score for match or mismatch. Usually matches get positive scores, whereas mismatches get relatively lower scores. A gap penalty function determines the score cost for opening or extending gaps. It is suggested that users choose the appropriate scoring system based on the goals. In addition, it is also a good practice to try different combinations of substitution matrices and gap penalties.
Initialize the scoring matrix. The dimensions of the scoring matrix are 1+length of each sequence respectively. All the elements of the first row and the first column are set to 0. The extra first row and first column make it possible to align one sequence to another at any position, and setting them to 0 makes the terminal gap free from penalty.
Scoring. Score each element from left to right, top to bottom in the matrix, considering the outcomes of substitutions (diagonal scores) or adding gaps (horizontal and vertical scores). If none of the scores are positive, this element gets a 0. Otherwise the highest score is used and the source of that score is recorded.
Traceback. Starting at the element with the highest score, traceback based on the source of each score recursively, until 0 is encountered. The segments that have the highest similarity score based on the given scoring system is generated in this process. To obtain the second best local alignment, apply the traceback process starting at the second highest score outside the trace of the best alignment.
The Smith–Waterman algorithm finds the segments in two sequences that have similarities while the Needleman–Wunsch algorithm aligns two complete sequences.
https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
WIKI
The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. It was one of the first applications of dynamic programming to compare biological sequences. The algorithm was developed by Saul B. Needleman and Christian D. Wunsch and published in 1970. The algorithm essentially divides a large problem (e.g. the full sequence) into a series of smaller problems, and it uses the solutions to the smaller problems to find an optimal solution to the larger problem. It is also sometimes referred to as the optimal matching algorithm and the global alignment technique. The Needleman–Wunsch algorithm is still widely used for optimal global alignment, particularly when the quality of the global alignment is of the utmost importance. The algorithm assigns a score to every possible alignment, and the purpose of the algorithm is to find all possible alignments having the highest score.
https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm
Maybe he buy a sport jacket.
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