by Trentism May 09, 2021
The Exponentialists:
The Exponentialists are a hypothetical class of super-affluent individuals who, according to this analysis, will emerge during the 2020s due to their strategic investments in companies poised to dominate the AI and robotics revolution. This revolution is projected to double global GDP within 10-20 years, with the majority of the resulting $100 trillion increase in economic output flowing into a small number of globally dominant, publicly traded companies. The Exponentialists, by owning equities in these companies (through direct investment, mutual funds, or other investment vehicles), will effectively own a substantial portion of this newly created wealth, experiencing a dramatic increase in their net worth as market capitalization multiples amplify the growth of the underlying companies. They are defined not simply by their wealth, but by the source of that wealth: early investment in the companies driving the AI and robotics revolution of the 2020s.
The Exponentialists are a hypothetical class of super-affluent individuals who, according to this analysis, will emerge during the 2020s due to their strategic investments in companies poised to dominate the AI and robotics revolution. This revolution is projected to double global GDP within 10-20 years, with the majority of the resulting $100 trillion increase in economic output flowing into a small number of globally dominant, publicly traded companies. The Exponentialists, by owning equities in these companies (through direct investment, mutual funds, or other investment vehicles), will effectively own a substantial portion of this newly created wealth, experiencing a dramatic increase in their net worth as market capitalization multiples amplify the growth of the underlying companies. They are defined not simply by their wealth, but by the source of that wealth: early investment in the companies driving the AI and robotics revolution of the 2020s.
* The early investors in companies like NVIDIA and Tesla, having foreseen the transformative power of AI and automation, are poised to become the core of the next generation of ultra-wealthy, the so-called "The Exponentialists."
* Concerns about widening wealth inequality are amplified by the potential rise of the "Exponentialists," a group whose fortunes are tied to the concentrated gains of a few dominant companies in the AI and robotics sectors.
* Concerns about widening wealth inequality are amplified by the potential rise of the "Exponentialists," a group whose fortunes are tied to the concentrated gains of a few dominant companies in the AI and robotics sectors.
by Trentism December 29, 2024
8 Figures is a reference to having accumulated >10 Million dollars but less than 100 Million.
10,000,000 = 8 digits
10,000,000 = 8 digits
by Trentism December 08, 2020
Acceleration of the death of vehicle OEMs due to the Inverse wrights law. For instance if their sales drop by 50% their cost to go up 15% and if their gross margin was originally 20% it would drop to 5% assuming they could not increase their pricing.
by Trentism January 16, 2022
A knowledge distillation approach that uses extreme loss function weighting to force neural networks to preserve semantic differences between distinct concepts while preventing mode collapse. The technique employs "nuclear" (extreme) lambda parameters that heavily weight diversity preservation over teacher alignment, ensuring that different input concepts produce genuinely different vector representations.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
The researchers implemented nuclear diversity in their knowledge distillation pipeline, using extreme lambda weighting of 6.0 for diversity preservation versus 0.02 for teacher alignment, successfully reducing semantic collapse from 0.998 to 0.324 cosine similarity between distinct concepts.
by Trentism July 09, 2025
by Trentism October 01, 2023
by Trentism May 09, 2021