Explore the assumptions

What would have to be true?

Inspect the proposed connections, follow costs across the system, and test how demand can offset efficiency.

The proposed model

Which connections need testing?

This proposed model has not been tested. Choose a factor to inspect the assumptions behind its connections.

Choose a factor

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Each factor has a list of proposed connections. On wider screens, lines show those connections; their width and position do not indicate strength or importance.

Question to investigate

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Read all 13 proposed connections
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The model guide explains its scope and limits. No causal ranking has been established.

Follow the whole system

Where did the entropy go?

Check where the costs and harms go, too. Energy losses, pollution, expense and risk need separate measures.

Include the full life cycle: extraction, manufacturing, delivery, use, maintenance and end of life.

Energy and physics

What flows in and out?

Usable energy, conversion losses, waste heat and available work. Include the entire system and state its operating conditions.

Materials and environment

What grows elsewhere?

Extraction, water, land, emissions, toxicity and replacement needs. Report the absolute totals as well as each unit of service.

People and deployment

Who carries the cost?

Affordability, safety, access, reliability, supply dependence and construction time. Name affected groups and missing evidence.

See the full set of candidate metrics

Choose relevant objectives and hard constraints for a specific service. Keep tradeoffs visible; no automatic combined score. Missing measurements are unknown.

Pareto frontier: options for which no feasible comparator is at least as good on every chosen objective and better on one. This only works with comparable boundaries and sufficiently resolved uncertainty. It does not automatically select a preferred option.

Read the comparison method

Two possible feedback loops

Does efficiency reduce total energy use?

An efficiency gain can support more research. It can also encourage more use. Both effects need to be measured.

Possible benefit

Energy supports research

  1. Better energy
  2. Cheaper, more efficient computation
  3. More scientific computation
  4. Better models, materials and engineering
  5. Potentially better energy
Possible offset

Lower costs can increase demand

  1. Cheaper computation
  2. More computation
  3. Greater aggregate energy demand
  4. More heat, infrastructure and materials
  5. Possible loss of the original benefit
Illustration, not a forecast

When does growth erase the saving?

Assume every task meets the same quality standard. Adjust the inputs to compare energy use before and after the change.

Energy use, compared with today

100%

The saving is exactly offset by task growth.

100% means no change. Below 100% means less energy.

0.80 × 1.25 = 1.00

A 25% increase in tasks erases a 20% saving per task.

Assumptions and limits

New energy ÷ baseline energy = (1 − saving per task) × (1 + task growth). It excludes manufacturing, hardware replacement, cooling changes, prices and other sectors. It does not estimate how demand responds to price or establish a lifecycle benefit.

Rebound: extra use caused by an efficiency-related change. Jevons-type backfire: induced use more than offsets savings. Task growth from unrelated causes is not automatically rebound. The benefit loop also requires useful, reproducible discoveries; more compute does not guarantee them.

The research process

Test, compare and update.

Investigate independently before sharing conclusions. Then compare evidence and agree on the next useful test. Keep unresolved disagreements visible.

ExpandChallengeTestConvergeRepeat

Start with several possible problem framings. Seek the cheapest test that could rule one out. Different AI roles do not, by themselves, create independent evidence.

Human and AI roles, independence and safety

Freeze the initial question and record each participant's sources, model or expertise, conflicts and exposure to earlier conclusions. Compare independent evidence summaries before sharing conclusions. Preserve dissent and source overlap. This static repository cannot enforce blind review or run a multi-AI system.

Humans are first-class researchers in every role. Hazardous physical work must remain theoretical until qualified professionals complete the appropriate review and authorize its scope.

Read the research protocol · Read the safety boundary
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Falsified hypotheses

None recorded yet. That means no project hypothesis has been falsified—not that any has survived a test.

Falsified-hypothesis index (JSON)

Results and replication

No completed project study or independent replication is recorded. Background sources explain terms; they do not validate the challenge.

Research index (JSON)
How evidence and confidence are labeled

Unresolved: the evidence cannot yet settle the bounded claim.

Plausible — experiment required: supporting evidence exists, but the needed test has not resolved the claim.

Falsified: evidence contradicts the registered claim within its stated scope.

Supported within tested conditions: documented independent replication supports the claim within named conditions.

Hypotheses, assumptions, simulations and experimental results are recorded separately. Confidence has a dated rationale; it is never a truth meter.