
American entrepreneur and businessman; founder and CEO of Nvidia
Missing: 4, 5, 8. Present digits repeat to show how many times they occur.
Eleven is a master number in Western practice: reduced it is 2, but the tradition holds it unreduced because a doubled 1 is read as intensified rather than merely summed. It is associated with sudden insight, unusual perception, and a nervous system that runs hot. The reading is consistent that master numbers are a burden before they are a gift — the perception arrives whether or not there is anywhere to put it.
The 8-1-6 row complete. Read as follow-through — the traditional marker of someone whose plans reliably become events.
A complete line in the grid.
A complete line in the grid.
Concentrated self-direction. Read as conviction that does not need external agreement — and does not always seek it.
Concentrated sensitivity. Read as perception so continuous it becomes hard to switch off.
Read as impatience with structure and maintenance. Associated with starting well and leaving the scaffolding to someone else.
The centre of the grid, so its absence is weighted heavily. Read as difficulty adapting mid-course — a preference for the plan already chosen over the plan now indicated.
Read as discomfort with power and money — not incapacity, but an unwillingness to claim authority openly.
Moolank 8 comes from the day of the month alone; Bhagyank 2 from the whole date. They differ, which is the ordinary case — the two numbers answer different questions.
Saturn. Labour, delay, consequence. Feared in popular practice more than any other, and read as the graha that makes people earn things.
Bhagyank uses the same arithmetic as the Western life path, so the two will always agree. That is not two traditions confirming each other — it is one calculation under two names.
"You benefit from the kindness of all the people that support you.
"Your organization should be the architecture of the machinery of building the product.
There's a language to proteins, there's a language to chemicals, and if we can understand the language and represent it in computer science, imagine the scale at which we can move, we can understand, and we can generate. We can understand proteins and the functions that are associated with them, and we can generate new proteins with new properties and functions. We can do that with generative AI now, now all of a sudden those words make sense and now they're connecting and fired up and are now applying it to all of their fields of their own companies and see opportunity after opportunity for themselves to apply it.
The inference, the scale of inference business has gone through a step function, no doubt, and the type of inference that is being done right now where you know that video will have generative AI added to it to augment the video either to enhance the background, enhance the subject, relight the face, do eye reposing, augment with fun graphics, so on and so forth. All of that generative AI work is done in the cloud and so video has generative AI. We know that there's imaging and 3D graphics for generative AI, video for generative AI.
The computing fabric that compute connects processors needs to be quite high speed. The faster the processors, the greater need for high speed computing fabrics and so it's a matter of scale and the effectiveness of the scale. For example, if you want to increase to 1000 processors, the linearity of that scale up would be less linear and it would plateau earlier if the interconnects were slower and so that's basically the trade-off. It's just a matter of how far can you scale and what is the effectiveness of the scaling, the linearity of the scaling.
Ultimately every company needs to have diversity and resilience, that resilience comes from diversity and redundancy and in order to a achieve diversity and redundancy so that every company can have greater resilience implies building fabs in the United States and elsewhere, and those fabs are incrementally more expensive. In the grand scheme of things, those have to be taken into consideration. And so, there's a price to be paid for diversity and redundancy and we invest ourselves in our company and every large company in order to have resilience. There's power redundancy, there's storage redundancy, there's security redundancy, there's all kinds of redundancy systems. Even organizations — sales and marketing are dovetailing each other so that they can have some diversity and some redundancy so that you have greater resilience, engineering does the same thing.
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