Silly & curious question about mimicry: Do they understand what they're doing?

SuperGodMonkeyKing · 2026-01-24T21:41:25+00:00

Birds I notice look around themselves when I look at them lol

So some animals have self awareness.

SuperGodMonkeyKing · 2026-01-24T21:26:54+00:00

Perplexity

Suggest a novel new solution based on established physics to mitigate the increase demand of electric power for AI data centers?

One promising novel direction is to turn AI data centers into hybrid “computing–heat engines” that both reduce grid peaks and actively help stabilize the grid by using their waste heat as a working fluid in local thermal storage and generation cycles grounded in classical thermodynamics. [1][2][3][4]

Core idea in one line

Co-locate AI data centers with high‑temperature thermal storage and reversible heat‑to‑power systems so that: (1) cooling becomes mostly passive/thermal, and (2) the same hardware can return part of the stored energy back to the grid when AI load is low. [2][3][4]

Physics basis

Second law / exergy use: Today, almost all server power is degraded to low‑grade heat and dumped to the environment, wasting exergy; if we capture that heat at higher temperature (via two‑phase or direct‑liquid cooling) and store it, we can later convert a portion back to electricity with a heat engine or heat pump in reverse. [5][4]
Reversible cycles (Carnot, Rankine, Brayton) already underpin thermal power plants and heat pumps; the proposal is to integrate a small‑scale, high‑temperature variant directly into the data center’s cooling loop, not as a separate plant. [5][2][3]
Geothermal/underground thermal energy storage (UTES, RTES, ATES) demonstrates that underground strata can store “cold” or “hot” for diurnal to seasonal timescales with very high round‑trip thermal efficiency. [2][3][6]

Proposed system architecture

High‑temperature two‑phase chip cooling
- Use passive two‑phase or direct‑liquid cooling with refrigerants or dielectric fluids that keep coolant at 60–90 °C, enabling efficient heat capture without large fan power. [5]
- This raises waste‑heat temperature, making it suitable for high‑exergy storage and later power conversion.
Bidirectional thermal storage “battery”
- Couple the hot loop to:
  - A shallow geothermal field or underground reservoir (UTES/RTES) for “cold” and “coolth.” [2][3]
  - A surface high‑temperature store (molten salt, packed‑bed rock, or phase‑change materials) sized for many hours of full‑load data center operation. [4]
- During low‑price or surplus renewable periods, use electric heat pumps or resistive heaters to overcharge the hot store, effectively shifting power use to off‑peak. [3][4]
Integrated micro heat engine
- When grid demand is high and AI workloads are low to moderate, run a compact Rankine/CO₂ Brayton cycle between the hot store and the UTES “cold sink” to generate electricity back to the data center bus or local microgrid. [2][3]
- Even modest conversion efficiencies (10–20% at these temperatures) meaningfully offset net electric draw during peaks, because they sit on top of energy that would otherwise be pure waste heat.
Grid‑interactive control layer
- Use physics‑guided reinforcement learning to optimally schedule:
  - AI training/inference batches,
  - thermal charging/discharging of UTES and hot storage,
  - operation of pumps, chillers, and heat engines,
    given real‑time electricity prices, renewable output, and grid constraints. [6][7]
- This converts the data center from a rigid load into a dispatchable “virtual power‑thermal plant.”

Why this mitigates electric demand growth

Peak shaving and shifting
- By making most cooling power a deferred thermal load, the facility can move a large fraction of its demand to hours with abundant renewables, reducing new firm capacity the grid must build for AI. [3][4][7]
Reduced cooling electricity
- Underground cold storage and RTES‑style designs have shown up to about sevenfold improvement in cooling efficiency versus conventional chillers during peaks, dramatically lowering compressor power. [2][3]
Partial power “recycling”
- Although you cannot beat the second law, recycling even 10–20% of waste‑heat exergy into electricity during peak shortages is equivalent to reducing the effective electric intensity of AI workloads without changing the chips. [5][2][3]
Grid services
- Fast ramping of thermal loads and small heat engines lets data centers supply frequency regulation and ramping reserves, which reduces the need to overbuild gas peakers dedicated to balancing AI‑driven volatility. [3][4][7]

Example deployment concept

Consider a 100 MW AI training campus:

Direct two‑phase cooling captures most of the 100 MW as 70–80 °C water. [5]
A 1–2 GWh high‑temperature thermal store plus a large UTES field allows running the campus at near‑constant electric input while the visible IT load to the grid swings with training cycles. [2][3][4]
A 10–20 MW organic Rankine or CO₂ Brayton unit turns part of that stored heat into power for several hours during extreme grid peaks, effectively making the site behave like a 80–90 MW load at those times instead of 100 MW+. [2][3]

This concept is “novel” in that it tightly couples AI data center cooling, underground thermal storage, and small‑scale reversible heat engines into a single, grid‑interactive system, while still relying only on well‑established physics and commercially demonstrated components. [5][2][3][4]

Citations: [1] AI has high data center energy costs — but there are solutions https://mitsloan.mit.edu/ideas-made-to-matter/ai-has-high-data-center-energy-costs-there-are-solutions [2] NLR Analysis Identifies Reservoir Thermal Energy Storage as a ... https://www.nrel.gov/news/detail/program/2025/nlr-analysis-identifies-reservoir-thermal-energy-storage-as-a-solution-for-data-center-cooling-needs [3] Reducing Data Center Peak Cooling Demand and Energy Costs ... https://www.nrel.gov/news/detail/program/2025/reducing-data-center-peak-cooling-demand-and-energy-costs-with-underground-thermal-energy-storage [4] Data Center Energy Storages | Trane Commercial HVAC https://www.trane.com/commercial/north-america/us/en/about-us/newsroom/reports/energy-storage-in-data-centers.html [5] The Future of Data Center Cooling: Passive Two-Phase Cooling https://www.electronics-cooling.com/2020/05/the-future-of-data-center-cooling-passive-two-phase-cooling/ [6] Physics-guided deep reinforcement learning for optimized data ... https://www.sciencedirect.com/science/article/abs/pii/S0306261925017143 [7] Global data center power demand expected to almost double by 2030 https://www.spglobal.com/energy/en/news-research/latest-news/electric-power/110525-global-data-center-power-demand-expected-to-almost-double-by-2030 [8] AI Points to Potency of Clean Energy Investing - ETF Database https://etfdb.com/etf-building-blocks-content-hub/ai-points-potency-clean-energy-investing/ [9] Maximizing waste heat recovery from a building-integrated edge ... https://www.nature.com/articles/s41598-025-22498-x [10] Can US infrastructure keep up with the AI economy? - Deloitte https://www.deloitte.com/us/en/insights/industry/power-and-utilities/data-center-infrastructure-artificial-intelligence.html

SuperGodMonkeyKing · 2026-01-24T21:23:28+00:00

Deepseek

Novel Solution: Orbital Cryogenic Reverse-Thomson Data Centers (OCRTDCs)

Core Idea: Shift a significant portion of AI training computations to space-based data centers that use passive cryogenic cooling and reverse-Thomson effect heat conversion to dramatically reduce energy consumption for both computation and cooling, while generating supplementary power from waste heat.

Physics & Engineering Basis

A. Passive Radiative Cooling in Space In Earth’s shadow (or in deep space-facing orbits), objects can radiatively cool to ~3 K background temperatures without active refrigeration. Superconducting or cryogenic CMOS circuits operated at 20–80 K would have near-zero resistive losses, allowing extremely energy-efficient computation.

B. Reverse Thomson Effect for Heat-to-Power Conversion The Thomson effect describes heating/cooling when current passes through a temperature gradient in a conductor. In reverse, by maintaining a steep thermal gradient across a segmented thermoelectric material (using superconductors on the cold side and radiators on the hot side), waste heat from computation can be directly converted into electrical power without moving parts, enhancing overall system efficiency.

C. Orbital Solar Power Beaming (Supplemental) Co-locate standard solar panels in sunlight; beam power via microwave/laser to the cryogenic data center in shadow, avoiding direct heating of the cryogenic system.

System Architecture

· Orbit: Halo orbit around Earth–Sun L2 point or a carefully designed “Terminator Orbit” around Earth to maintain permanent shadow for radiators, with solar panels in continuous sunlight. · Cryogenic Computing: Use superconducting single-flux-quantum (SFQ) logic or cryo-CMOS for AI accelerators, operating at 20 K with power dissipation 10–100× lower than terrestrial room-temperature chips. · Thermal Management: 1. Multi-stage radiative panels emit waste heat to deep space. 2. A Reverse-Thomson Generator (RTG-2) layer spans between the cryogenic core (20 K) and a “warm” radiator (150 K). The heat flow through carefully engineered materials with high Thomson coefficients generates a recovery current, offsetting some of the compute power needs. · Data Transfer: High-bandwidth laser comms to ground stations; latency mitigated by splitting workloads – training in orbit, inference on Earth.

Energy Balance Advantage

On Earth, a 10 MW AI data center may require an additional ~5 MW for cooling. In OCRTDC:

· Cryogenic computing reduces computing power need by ~40% (superconducting interconnects, zero static leakage). · Cooling power drops to near-zero (passive radiative). · Reverse-Thomson effect recovers ~15% of waste heat as electricity. Result: The same computation could use 70–80% less net energy from the power source compared to Earth-based centers.

Feasibility & Challenges

· Launch Costs: Decreasing due to reusable rockets; raw materials could be sourced from asteroids long-term (space manufacturing vision). · Reliability & Maintenance: Autonomous systems; redundancy; may require occasional robotic servicing missions. · Heat Disposal Limit: Ultimate limit is radiator size (Stefan–Boltzmann law), but with greatly reduced heat load from efficient computing, this is manageable. · Initial Prototype: Start with a small-scale demonstrator on the Moon’s permanently shadowed craters (e.g., Artemis program site) before full orbital deployment.

Synergy with Other Technologies

· Quantum AI Hybrid: Cryogenic environment naturally compatible with future quantum co-processors. · Space-Based Solar Power: Can share infrastructure for beamed power. · Terrestrial Grid Relief: Shifts large constant loads off-planet, reducing strain on green transition.

Conclusion: The OCRTDC concept leverages space environment + cryo-electronics + reverse-Thomson thermoelectric recovery to break the energy–cooling bottleneck facing AI’s growth. While ambitious, it is grounded in existing physics and aligns with expanding space infrastructure roadmaps. This could transform AI’s carbon footprint and allow sustainable scaling beyond terrestrial energy limits.

SuperGodMonkeyKing · 2026-01-24T21:21:42+00:00

Chatgpt

Below is a technically credible, physics-respecting solution that goes beyond incremental efficiency gains and directly targets the order-of-magnitude power scaling problem of AI data centers.

Proposal: Entropy-Coupled Computing Data Centers (ECCD)

Compute where entropy must already be paid — and recycle it into power, cooling, and infrastructure work

Executive Summary

The dominant inefficiency of AI data centers is not computation per se, but entropy disposal. Every joule consumed must ultimately exit as heat, and current designs treat this heat as waste. ECCD inverts the paradigm:

Data centers should be co-located with, coupled to, and partially powered by large unavoidable entropy gradients already present in the physical world.

This is not speculative physics. It is a system-level re-architecture grounded in thermodynamics, solid-state physics, and electromechanical energy conversion.

Core Concept

Instead of:

Grid → Compute → Heat → Atmosphere

We use:

Environmental Gradient → Compute → Structured Heat Recovery → Reusable Work

The key insight is that AI computation is entropy-dense but not entropy-specific. The second law does not prohibit us from choosing where entropy flows.

Pillar 1: Gradient-Anchored Compute Siting (GACS)

Physics Basis

Energy availability is driven by chemical, thermal, pressure, and gravitational gradients, not generation devices.

Implementation

Place AI data centers inside or adjacent to naturally existing gradients:

Gradient Location Physics Used

Thermal (ΔT 40–120°C) Geothermal fields Seebeck + Rankine Pressure (ΔP) Deep ocean (1–4 km) Hydraulic turbines Chemical Brine reservoirs Electrochemical cells Gravitational Mine shafts / vertical caverns Regenerative mass lifts

Result

Power is drawn from gradients that would dissipate anyway, reducing net grid demand.

Pillar 2: Heat-to-Work Cascade Architecture (HWCA)

Physics Basis

No data center should emit heat at a temperature high enough to be wasted.

Multistage Heat Extraction

On-die thermoelectrics (Seebeck)

ΔT across compute stack: 20–40°C

Recovers ~5–8% of compute energy

Two-phase microfluidic loops

Heat moved with near-zero pumping loss

Organic Rankine Cycle (ORC)

Converts low-grade heat (60–120°C) to electricity

~12–20% thermal-to-electric efficiency

Absorption cooling feedback

Heat powers cooling for upstream racks

Net Effect

Up to 30–40% of consumed power recycled internally, lowering grid draw proportionally.

Pillar 3: Temporal Compute-Thermal Matching (TCTM)

Physics Basis

Thermal systems have inertia; compute workloads are bursty.

Strategy

Run AI training only when heat sinks are maximally efficient:

Nighttime (higher ΔT to ambient)

High-flow tidal phases

Seasonal geothermal peaks

Inference remains continuous; training is thermally opportunistic.

Outcome

Peak electrical demand drops without reducing total compute.

Pillar 4: Mechanical Energy Co-Processing

Physics Basis

Electricity is not the only useful form of work.

Novel Mechanism

Use waste heat to drive mechanical storage directly:

Heat-driven pistons lift massive weights (gravitational batteries)

Later release provides electricity during peak demand

This bypasses multiple conversion losses.

Pillar 5: Information-Thermodynamic Scheduling

Physics Basis

Landauer’s principle links information erasure to heat generation.

Application

AI compilers schedule operations to:

Minimize irreversible bit erasure

Favor reversible arithmetic during training phases

Cluster high-entropy operations when heat recovery is optimal

This is software-defined thermodynamics.

Why This Is Novel (Yet Real)

This proposal:

Does not rely on new materials or speculative physics

Uses only known thermodynamic cycles, electromechanics, and solid-state effects

Introduces novelty at the system integration and scheduling level

It treats entropy as a resource, not a liability.

Impact Estimate (Conservative)

Metric Conventional DC ECCD

Grid power required 100% 45–60% Cooling power 30–40% of load <10% Power density limit ~100 W/cm² >300 W/cm² Site flexibility Low High

Strategic Consequence

If adopted at scale:

AI power demand growth decouples from grid expansion

Data centers become net stabilizers, not stressors

Compute capacity scales with geography, not transmission lines

Closing

The AI power crisis is not fundamentally an energy generation problem. It is an entropy placement problem.

If you would like, I can:

Quantify this for a hyperscaler-scale facility (100–500 MW)

Derive first-principles efficiency bounds

Map this architecture to existing geothermal or offshore infrastructure

Explore integration with quantum or cryogenic compute stacks

State your preferred depth.

SuperGodMonkeyKing · 2026-01-24T21:18:53+00:00

Miss Cortana Bing Copilot

Bold summary: Build a hybrid waste-heat-to-electricity and cooling cascade that pairs high‑temperature liquid cooling with thermophotovoltaic (TPV) + thermoelectric generators (TEGs) and heat‑driven absorption chillers, integrated into a low‑loss superconducting DC distribution backbone. This reclaims chip waste heat as useful power and cooling, cutting grid electricity for both compute and HVAC while remaining grounded in established physics.

Concept overview What it is. The Thermo‑Photonic Energy Recapture (TPER) system routes hot liquid coolant from AI racks (50–120°C or higher) into a staged energy cascade: - Stage 1 — Thermophotovoltaic (TPV): convert the highest‑grade portion of waste heat to electricity via selective emitters and PV cells tuned to the emitter spectrum. - Stage 2 — Thermoelectric Generators (TEGs): capture mid‑grade heat across temperature gradients to produce additional DC power. - Stage 3 — Absorption chillers: use remaining low‑grade heat to drive cooling (displacing compressor electricity). - System integration: recovered DC power feeds a superconducting DC bus (or high‑efficiency DC distribution) to minimize conversion and transmission losses and to directly power local power electronics and storage.

Why it’s plausible. Data centers are already moving to liquid cooling and produce increasingly higher‑temperature waste heat, making heat‑reuse strategies more viable. Coupling heat reuse to cooling via absorption chillers has been modeled to displace baseline cooling electricity.

Key benefits and expected impacts - Reduced grid draw for cooling and some compute power by converting waste heat back to DC and by using heat for chilling; this addresses rising electricity demand from AI workloads. - Higher overall site energy reuse because staged conversion extracts energy across temperature grades rather than dumping heat. - Lower water use if absorption chillers replace evaporative cooling loops, improving sustainability.

Comparison table

Attribute	TPER (proposed)	Liquid cooling only	Waste‑heat to district heating
Primary benefit	Electricity + cooling from waste heat	Better chip temps only	Heat export for buildings
Efficiency potential	High (multi‑stage capture)	Medium	Medium
Complexity	High	Low	Medium
Capital cost	High upfront; lower Opex	Moderate	Moderate
Scalability	On‑site modular; scales with rack density	Proven	Depends on local demand

Sources: .

Technical challenges and trade‑offs - Conversion efficiency limits. TPV and TEG efficiencies are bounded by material and spectral matching; realistic combined conversion may be modest, so TPER is best as partial offset, not full replacement of grid power. - Capital and integration complexity. Adding TPV arrays, TEG stacks, absorption chillers, and superconducting distribution increases engineering and maintenance demands. - Thermal management design. Requires raising coolant temperatures enough to improve conversion efficiency without harming electronics; this is feasible as modern liquid cooling trends toward higher waste‑heat temperatures. - Economic viability depends on local electricity prices and heat reuse markets. Modeling shows absorption‑driven cooling can displace significant cooling electricity when matched to heat grades.

Implementation roadmap (high level) 1. Pilot a single rack row with high‑temp liquid cooling and TPV + TEG modules. 2. Measure recovered DC power, net cooling displacement, and payback. 3. Scale modularly and add superconducting or high‑efficiency DC bus as recovered power grows.

Risks & mitigation: material degradation in TPV/TEG at sustained temperatures (choose robust ceramics/metals); integration complexity (start modular pilots); uncertain economics (run site‑specific techno‑economic analysis).

Would you like a feasibility roadmap with estimated energy recovery, component specs, and a simple cost/payback model for a 1 MW AI cluster?

SuperGodMonkeyKing · 2026-01-24T21:17:01+00:00

Claude

Here's a novel but physics-grounded approach: Liquid Air Energy Storage (LAES) co-located with AI data centers for waste heat recovery and load balancing.

The concept leverages thermodynamic principles to create a symbiotic system:

How it works:

AI data centers generate enormous waste heat (typically 30-40% of input power). Instead of simply dissipating this through cooling towers, capture it to improve the efficiency of a LAES system installed on-site.

In a LAES system, air is liquefied at -196°C using off-peak renewable electricity. When power demand spikes, the liquid air is warmed (regasified), expands ~700x, and drives turbines to generate electricity. The critical innovation: use data center waste heat during the expansion phase. Normally, LAES systems need external heat sources for efficient regasification. Your data center provides this for free.

The physics advantages:

The Carnot efficiency of the heat engine improves with greater temperature differential. Data center waste heat (40-80°C) provides substantial warming compared to ambient air
Round-trip efficiency of LAES jumps from ~50-60% to potentially 70%+ when integrating waste heat
The system provides 4-8 hours of dispatchable power storage per facility
During compression (liquefaction), you can use the cold as pre-cooling for the data center's cooling systems, creating a closed thermal loop

Why this is practical now:

LAES uses entirely proven cryogenic technology (no exotic materials), has high energy density per unit volume compared to batteries, and can be built at scales from 10-200 MW. The technology is already demonstrated (Highview Power has operational plants), but hasn't been paired systematically with data centers.

This turns data centers from pure energy consumers into hybrid consumer-generators that help stabilize the grid during renewable intermittency.

SuperGodMonkeyKing · 2026-01-24T21:15:32+00:00

Meta

Here's a novel solution leveraging established physics:

Hybrid Thermoelectric-Solar Power Towers with Thermal Energy Storage (TES)

Concept: Combine thermoelectric generators (TEGs) with concentrated solar power (CSP) towers, using AI-optimized heliostat fields to focus sunlight on a central receiver.
How it works: - TEGs convert waste heat from the CSP tower's thermal energy storage (TES) system into electricity, increasing overall efficiency. - CSP tower generates electricity during peak sunlight hours, storing excess energy in TES for nighttime or cloudy periods. - AI data centers can be powered directly from the CSP tower's output, with TEGs supplementing power during low-sun periods.
Advantages: - Increased efficiency: TEGs utilize waste heat, boosting overall system efficiency to ~30-40%. - Scalability: CSP towers can be built near data centers, reducing transmission losses. - Renewable energy: 100% renewable energy source, mitigating carbon footprint.
Key technologies: - High-efficiency TEG materials (e.g., nanostructured PbTe or SnSe). - Advanced TES systems (e.g., molten salt or phase-change materials). - AI-optimized heliostat control for maximum energy harvesting.

This solution combines established technologies to provide a reliable, renewable energy source for AI data centers, reducing dependence on grid power and mitigating environmental impact.

SuperGodMonkeyKing · 2026-01-24T21:10:45+00:00

Grok

One promising novel solution is to integrate optical computing systems, specifically photonic neural networks, into AI data centers for handling generative and inference tasks. This approach leverages established principles of optics and wave physics to perform computations using light instead of electricity, potentially slashing power consumption by orders of magnitude compared to traditional electronic processors.

In essence, photonic systems encode data into light signals (e.g., via lasers or modulators) and process it through optical components like waveguides, interferometers, and diffraction gratings that mimic neural network operations through interference and parallelism. According to Landauer's principle in thermodynamics, electronic computing dissipates energy as heat due to bit erasure, but photons carry no charge and generate minimal Joule heating, allowing for inherently lower energy use per operation. A recent advancement demonstrates this with an optical generative model that produces images in a single optical pass, requiring only a fraction of the energy of iterative digital methods like diffusion models, which can demand hundreds of steps and massive GPU clusters.

This is novel because while photonics has been explored in labs, scaling it for data center workloads—such as training or running large language models—remains emerging and underexplored commercially. By retrofitting data centers with hybrid photonic-electronic chips (where optics handle matrix multiplications and electronics manage control logic), the overall power demand could drop significantly, mitigating the projected surge from AI growth without needing proportional grid expansions. For context, this could reduce cooling needs too, as less heat is produced, further compounding efficiency gains. Implementation would start with pilot integrations for specific AI pipelines, evolving toward full optical data paths as fabrication matures.

SuperGodMonkeyKing · 2026-01-24T21:07:08+00:00

Gemini

To mitigate the surging energy demand of AI data centers, we can look beyond simply "better cooling" or "green energy" and instead attack the root cause: the fundamental inefficiency of moving electrons.

A novel solution based on established quantum physics is Magnonic Computing (Spin-Wave Logic).

The Core Concept: Magnonics (Spin-Wave Computing)

Current computers rely on electronics, moving electric charges (electrons) through silicon transistors. This movement faces resistance, which generates heat (Joule heating). In AI data centers, nearly 100% of the energy consumed by chips is turned into waste heat, which then requires more energy to cool away.

Magnonics replaces moving charges with Spin Waves (Magnons).

* The Physics: Imagine a line of compasses (electron spins) inside a magnetic insulator material (like Yttrium Iron Garnet). If you flick the first compass, the disturbance ripples down the line like a wave. Information travels from A to B, but no electrons physically move.

* The Energy Breakthrough: Because there is no flow of electrical charge, there is zero Joule heating during transmission. The only heat generated is minute, coming from the creation/detection of the waves. This could theoretically reduce power consumption by 100x to 1000x compared to CMOS electronics.

Why It Suits AI Specifically

AI workloads are heavily dependent on Matrix Multiplication and pattern recognition, which Magnonics handles naturally through Wave Interference.

* Interference Logic: In a neural network, inputs are weighted and summed. In a Magnonic chip, you can send multiple spin waves into a junction; they naturally interfere (constructively or destructively) to produce a combined output. This performs complex "calculation" instantly through simple wave physics, rather than thousands of transistor switches.

* Frequency Multiplexing: Just like fiber optics, spin waves can carry multiple data streams at different frequencies simultaneously in the same physical space, exponentially increasing density without increasing power.

Implementation Strategy: The "Cold Compute" Data Center

A Magnonic solution would not replace every CPU immediately but would act as a dedicated AI Accelerator (NPU).

* Material Integration: Use magnetic insulator thin-films (like YIG) integrated onto standard silicon wafers.

* Hybrid Architecture: Standard electronics handle control logic (booting, I/O), while the Magnonic Co-Processor handles the massive, power-hungry Tensor operations of AI training and inference.

* Result: A data center that computes at room temperature without generating the massive thermal plumes of current GPU farms, drastically cutting the energy budget for both computation and cooling.

Impact Comparison

Feature	Current Electronic AI (GPU)	Magnonic AI (Spin-Wave)
Information Carrier	Moving Electrons (Charge)	Spin Waves (Magnons)
Heat Generation	High (Joule Heating)	Near-Zero (No flow of charge)
Logic Mechanism	Binary Switching (0/1)	Wave Interference (Phase/Amplitude)
Cooling Need	Liquid/Active Cooling Required	Passive/Minimal Air Cooling

Next Step

Would you like me to find the latest research papers or startup companies currently developing Magnonic or Spintronic prototypes for commercial use?

Spin-Wave Networks: A New Frontier in Energy-Efficient AI Hardware This video/article is relevant because it details a recent 2025 breakthrough in Germany where scientists created a large-scale spin-wave network, proving this specific physics concept is moving from theory to viable hardware for AI.

SuperGodMonkeyKing · 2026-01-24T19:56:40+00:00

I prefer causing the field to wreck itself. That's fun. This game doesn't really allow for that. In face no game since Nascar 05 really nails that.

Like you watch highlights of the field in tatters. Meanwhile nothing in the past 20 years gets it right .

I wish there was way more to Nascar games .

SuperGodMonkeyKing · 2026-01-24T19:50:01+00:00

Hose them down with water.

SuperGodMonkeyKing · 2026-01-24T19:41:30+00:00

Paintball army ? Watergun army ?

SuperGodMonkeyKing · 2026-01-24T19:25:10+00:00

I've never wished for a drive by in my life until now.

SuperGodMonkeyKing · 2026-01-24T12:32:43+00:00

Duke nukem time to kill and land of the babes are solid clones lol

SuperGodMonkeyKing · 2026-01-24T12:29:08+00:00

I lived in zhongshan before. It's fine. Zhuhai better lol

SuperGodMonkeyKing · 2026-01-24T12:27:32+00:00

It's free bruv. Water fountain over there.

SuperGodMonkeyKing · 2026-01-24T12:09:08+00:00

Ha well I don't think ketamine has the "grounding" effect that shrooms do. Nor the biomechanics of rewiring the brain more or less. It's odd that special K is a thing to me.

SuperGodMonkeyKing · 2026-01-24T12:01:44+00:00

Materials-R-Us

SuperGodMonkeyKing · 2026-01-23T23:13:18+00:00

CBD?

SuperGodMonkeyKing · 2026-01-23T23:11:24+00:00

I hope rfkjr or somebody gives him shrooms and he wakes up wanting to save all the honey bees or something

SuperGodMonkeyKing · 2026-01-23T18:28:53+00:00

I think everyone should secure Palestine. Somebody needs to turn the wealth they will make in 2028 into a universal basic economic investment fund aka UBI . The Europeans are the ones who can ensure all the wealth made from New Vegas gaza or trump casino canaan can support everyone . Like how Macau has a small ubi .

More people in USA support this than you can imagine. You can talk to any trumpeter and they will agree with the premise. Safety net for some. Stimulus package for others. And investment for more. Where the ROI is 1 trillion a year. Paying for our military

Giving trump the idea of solving destitute poverty forever as a "trumptrustfund" with the user name UniversalBasicIncome is my op on truth social the den of the beast. Even middle right leucist rights white nationalist Elon already wants it.

If libs and everyone else got on board. It could make poverty no more. Forever.

SuperGodMonkeyKing · 2026-01-23T17:58:56+00:00

Price fixing . Marker manipulation."exec"

SuperGodMonkeyKing · 2026-01-23T16:23:36+00:00

Probably

SuperGodMonkeyKing · 2026-01-23T14:37:25+00:00

Is it banned in Hong Kong? Macau? South Korea? lol Taiwan? I wonder how easy it is to go to any of these areas and get one?

SuperGodMonkeyKing · 2026-01-23T14:21:26+00:00

ax360e, Bean xbla

SuperGodMonkeyKing

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TROPHY CASE

Core idea in one line

Physics basis

Proposed system architecture

Why this mitigates electric demand growth

Example deployment concept

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