Yep.
Our own secret “Operation Paperclip” brought over 1500 Nazi Scientists, Engineers, and Technicians into the USA to work on rockets, space, etc.
This included V2 rocket scientists like Werner Von Braun, known members of the SS and SA, and war criminals, all of whom were assimilated into American life.
Given that, we don’t have much room to criticize Argentina or the Vatican in that regard.
Yeah but we’re talking specifically Argentina in this instance. It’s why it’s funny.
Not that the United States doesn’t harbor and support super villains involved in AI, but that’s a lot more depressing
Each day I tend to think there is a bubble aspect just now. Not saying AI will die or
go away ; it will still be the thing.
I’m looking at it more from a chip design perspective and what is in the pipeline for
Nvidia, Intel, and TSMC. What may be necessary is next generation devices that
can provide the ultimate compute power needed for advanced AI. Is that quantum computing or more exotic materials or 3D ? The environmental problems with “as is”
technology may be coming too great of hurdles to ignore. Interested in the “data centers
in space” concepts that Musk and China now seem to be pursuing.
(My personal problem is how to somewhat divest of my semiconductor stocks that are in taxable accounts in the near term)
And of course, the obligatory AI summary…
The Limits of Conventional Miniaturization
As transistors shrink to just a few nanometers (approaching atomic scales), several fundamental laws of physics start to break the technology: [1, 2]
Quantum Tunneling: When transistors get too small, the “dams” meant to stop electricity become too thin. Due to quantum physics, electrons can essentially “teleport” or tunnel through solid barriers, causing transistors to leak electricity and corrupt data even when turned off. [1, 2]
Extreme Heat: As components get smaller, the microscopic copper wires connecting them become thinner and longer. This increases electrical resistance and generates intense heat that is incredibly difficult to dissipate, impairing overall performance. [1, 2]
Escalating Costs and Environmental Impact: Maintaining the pace of miniaturization requires multi-billion-dollar manufacturing facilities using highly specialized equipment. Additionally, the process demands massive amounts of energy and utilizes chemicals that persist in the environment, making traditional scaling increasingly unsustainable. [1]
Where Computing Goes from Here
To overcome these physical roadblocks, researchers and engineers are fundamentally changing how chips are designed and built: [1]
3D Chip Stacking: Instead of sprawling out and making transistors smaller, engineers are building upward. By stacking silicon circuits in multiple layers using ultra-thin membranes and low-temperature manufacturing, chips can achieve super-fast memory access and massive density without hitting quantum hurdles. [1, 2]
New Materials: To bypass the limits of traditional copper and silicon, scientists are exploring materials like graphene and ruthenium, which have lower electrical resistance and generate far less heat. [1, 2]
Compute-In-Memory: Currently, transferring data between memory and processing units takes a lot of time and energy. New ultra-low-power designs handle computation directly within the memory device, which could drastically reduce data center energy requirements. [1, 2]
More-than-Moore Architectures: This approach integrates multiple functions (logic, memory, and sensors) into a single, compact device rather than simply trying to shrink transistors. [1]
The era of merely making flat, 2D transistors smaller is largely coming to a close, but the era of 3D, specialized, and highly integrated computing is just getting started. [1, 2]
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