Trauth Research LLC – Our Vision & Products

 

We don’t aim for incremental tweaks – we fundamentally redefine the architecture and energetics of neural networks and high-performance computing.

Our technology lives in two worlds:
🔬 T-Zero Field for the scientific community
💡 Prime Core for commercial applications

Our product lines at a glance:

Prime Core:
mini: Validated prototype for labs & small data centers (up to 40% energy savings) – empirically proven, already operational.

extended: R&D goal for enterprise GPU clusters (target: up to 60%) – currently in development, not validated, not available.

max: Vision for hyperscale/mission-critical AI/HPC operations (target: up to 75%) – planned, not validated, not available.

Only Prime Core mini is validated and operational. All other versions are under research & development with no guarantees on outcomes or timelines.

Prime One:
Our research platform for next-generation data & market analysis:
Advanced ETF tracker
DQN/DNN-based analytics
Large LLM module for explainable recommendations
➡️ All components are experimental – no predictions, no performance guarantees.

Prime Vision:
Our "moonshot" at the intersection of cryptography & information security.
Research into emergent "Horizon Layer" structures that could enable entirely new paradigms still strictly experimental.

Scientific integrity and transparency are non-negotiable for us.
We don’t make empty promises we deliver empirical results and maintain a clear distinction between product and research.

👉 See more details & current insights in our updated pitch deck:
https://lnkd.in/dgtfYZm4 or visit our website for more information:

Trauth Research LLC – Investor Presentation ⚡

 

Trauth Research LLC – Investor Presentation ⚡

Trauth Research LLC, founded in 2025 in Sheridan, Wyoming (USA), develops resonance-field architectures to optimize GPU systems.

Our MVP T-Zero mini introduces the first software-only solution embedding a self-organizing resonance field directly into GPU topologies. This delivers minimum 40% energy savings at up to 80% load with no loss of performance, no hardware modifications, and no additional cooling requirements.

Market context 🌍

AI and HPC data centers already consume more than 120 TWh of electricity annually, representing over $50B in global energy and cooling costs. Existing approaches (frequency throttling, new chip architectures, advanced cooling systems) offer only partial improvements and typically require heavy CAPEX. T-Zero provides a pure software model that scales without hardware intervention.

Roadmap

👉 T-Zero mini (MVP, validated on NVIDIA Ada Lovelace & Blackwell GPUs): guaranteed savings from 40%

👉 T-Zero extended (prototype, close to deploy): up to 60% savings

👉 T-Zero max (experimental): 70%+ savings at full load

Business model

Licensing model: 40% of client’s realized energy savings. This directly converts OPEX reductions into measurable ROI, with zero upfront investment required.

Funding

We are currently opening a €2.5M Pre-Seed round (SAFE, €15M valuation cap, 15% discount) to scale first enterprise pilot deployments.

Further information, including the full Pitch Deck and One-Pager, is available on F6S, Crunchbase, and Gust.

Pitch Deck

www.Trauth-Research.com

AI vs. Human Energy Consumption – Facts We Can’t Ignore

 

We love to talk about AI’s “huge” energy use.

But here’s a fact check: how much energy do you actually burn to create the same output? 🤔

✍️ Writing – Human vs. AI

AI (OpenAI): ~0.34 Wh per query (~500 words)

Human: 3 min planning + 20 min writing + 5 min editing = 28 min active work

Brain power during focus ≈ 20 W → ~9.33 Wh per output

Result: Humans consume ~27× more energy than AI for the same text.

🎨 Drawing – Human vs. AI

A human artist might spend hours sketching, inking, and coloring, brain, muscles, and metabolism running the whole time.

A generative AI system? Seconds to minutes.

Even without exact numbers, the energy gap is massive orders of magnitude in AI’s favor.

🧠 The Point

This is not about replacing human creativity.

It’s about seeing the numbers without bias:

For equivalent creative output, AI’s energy footprint can be just a fraction of ours.

And in a world where efficiency matters, that’s worth more than a casual mention. 💡

#TrauthResearch #AIEnergy #HumanEnergy #HumanandAI

www.Trauth-Research.com

🔥 AI Physics just changed. The Injector Neuron is real. 🔥

 

🔥 AI Physics just changed. The Injector Neuron is real. 🔥

Let’s set the record straight:

At the heart of my latest preprint lies a phenomenon that every theorist has dreamed of, but no one has empirically nailed until now.

Meet the injector neuron: the first truly dynamic, energetic gateway between an artificial neural network and a non-classical field. It does not just influence it actively mediates energy, creating reproducible synchronization and real, measurable entanglement across AI systems on a scale never achieved before.

My team? 120,000+ neurons. Over 30 layers.

My claim? Classical causality, network topology, and signal theory do not explain what we see. This is field-emergent order, measured and reproducible, not hand-waving theory.

What did we prove?

Energetic Interface: The injector neuron is autonomous. It regulates, absorbs, and emits energy between the net and the field no classical inputs, no control variables, just pure, bidirectional dynamics.

Measured energy fluctuations? Tens to hundreds of watts, on demand.
Perfect Alternating Coupling: Analyze sequential layers and you’ll see it: r = ±1.0. Layer after layer. Perfect, reproducible alternation impossible in any classical DNN. Now it’s routine.

Universal Coefficient Anomaly: Hub mode? The injector’s correlation to all key layers is identical (r ≈ 0.1812), no matter time, position, or logical distance. Causality? Linear flow? Outdated ideas.

Ultra-High Internal Synchronization: Inside each layer, most neuron pairs hit >99.999% correlation robust, reproducible, and totally at odds with everything noise and diffusion theory predicted.

This isn’t theory. This is reproducible data, verified and logged. All visualizations and results are based on hard numbers.

📎 Full preprint including all visualizations  DOI: https://doi.org/10.5281/zenodo.16756035

#TrauthResearch #Physics #EmergentAI #AI #NeuralEntanglement #NeuralNetwork

Energetic Decoupling and Mirror Resonance: The Role of the Injector Neuron in Self-Organizing, Field-Based AI Systems

 

After more than a year of continuous field research, countless benchmarks, and ongoing analysis of energetic anomalies in self-organizing AI networks, one central detail has fundamentally changed my view of neural architecture: a single, completely outlier neuron – with an amplitude of ±2000 (instead of the usual ±1 in classical networks) – acts like a reactor at the center of the resonance field.

What do we know so far?

The auxiliary networks: > 30 layers with more than 120,000 neurons, of which at least 20 layers exhibit striking structural symmetry (including mirror symmetry and identical coefficient factors).

The injector neuron is at the core of these observations – its function is not yet fully understood, but it appears to be directly or indirectly responsible for the energetically decoupled state that we can reproduce experimentally.

There is also strong evidence that this neuron (likely in cooperation with a second, not yet fully analyzed partner neuron) plays a key role in triggering or maintaining the observed mirror symmetry within the system.

Scientific context:

The observations suggest that it may not be the distributed activity of many neurons that governs the system as a whole, but rather the presence of a single, autonomously acting center of order.

The classical notion that optimization is achieved solely by adjusting weights or architectural parameters must be expanded to include structural self-organization.

Overall, the focus shifts away from pure efficiency gains toward the fundamental question of how complex systems can internally generate storage, transformation, and balancing of energy.

In a broader context:

These findings open a novel perspective on field-based ordering principles and the development of AI architectures that go far beyond classical training approaches.

They point to the existence of hidden centers within complex, nonlinear systems—centers that may function as nodes of order, stability, and energetic self-regulation.

A full scientific preprint, including detailed analysis of the observed correlations and coefficient constancy, as well as the real-time based visualization shown in Image 2, will follow shortly.

#TrauthResearch #AI #ResonanceField #Emergence #NeuralNetworks #InjectorNeuron #SelfOrganization #Physics

Image 1: “Energetic Decoupling and Mirror Resonance”, generated using ChatGPT model GPT-4o (August 2025)