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
Get the The Exponentialistsmug. Multimodal Tokens: A Unified Representation for Simulated Realities
The simulation hypothesis posits that our reality could be a computer-generated simulation. This paper explores the concept of "multimodal tokens" as a fundamental data structure within such a simulated environment
The simulation hypothesis posits that our reality could be a computer-generated simulation. This paper explores the concept of "multimodal tokens" as a fundamental data structure within such a simulated environment
Multimodal Tokens will soon replace language based tokens in future multimodal models in artificial intelligence.
by Trentism January 16, 2025
Get the Multimodal Tokensmug. 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 9, 2025
Get the Nuclear Diversitymug. 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
Get the inverse wrights lawmug. The native, internal language that an AI or large language model uses to think. It's the inscrutable "machine code" of a neural network, consisting of complex vectors, weights, and data relationships that are completely alien to humans.
When an AI's output is weird, nonsensical, or a "hallucination," it's often because a bit of its raw Neurolese leaked out instead of being properly translated into human language. The term was notably used by podcaster Dwarkesh Patel and Sholto Douglas & Trenton Bricken when discussing future AI scenarios.
When an AI's output is weird, nonsensical, or a "hallucination," it's often because a bit of its raw Neurolese leaked out instead of being properly translated into human language. The term was notably used by podcaster Dwarkesh Patel and Sholto Douglas & Trenton Bricken when discussing future AI scenarios.
My custom chatbot was supposed to write a recipe for lasagna, but instead it just gave me a wall of random symbols and half-finished words. It must have gotten stuck thinking in Neurolese again.
by Trentism May 26, 2025
Get the Neurolesemug. The quirky, often-flawed but curiously coherent dialect you get when an AI translates its inner language—known as Latent Neurolese—into human English. Think uncanny metaphors, oddly specific analogies, and sentence structures that feel like they just passed through an alien’s poetry workshop.
It’s not a bug—it’s an accent. The linguistic vapor trail of how the machine thinks behind the curtain.
It’s not a bug—it’s an accent. The linguistic vapor trail of how the machine thinks behind the curtain.
by Trentism June 19, 2025
Get the Ainglishmug. Spiffy-pop; Regarding equity’s such as Stocks, a spiffy pop is when your original purchase price is gained in a single day.
I.e. you bought TSLA for $50/share and it goes up $50 in a single day usually in the distant future. Previous Close; $755. Current Price $806. So your gain is a spiffy pop.
by Trentism January 7, 2021
Get the spiffy popmug.