AuthOrigin — Resource Analysis

The Butter Budget

To calculate exactly how much "time and butter" an AI consumes to answer a query, you have to look at the hard physical physics of data centres and the token allocation budgets set by tech companies.

When you submit a prompt, the system allocates a locked, tiny sliver of physical resources to your request.

Chapter 01

The "Time" Budget

The Millisecond Matrix

Under the Time Hierarchy Theorem, a standard LLM query is restricted to a very shallow computational tier.

2–5s

Total processing time

The Execution Limit — For a standard prompt, the model is allowed roughly 2 to 5 seconds of total processing time.

15–30ms

Per token calculation

The Token Speed — The text calculator allocates a tiny fraction of a second to calculate each individual word (token).

Hard cut-off

No clock speed change

The Hard Cut-Off — If you ask a standard model a question requiring deep mathematical logic, it cannot change its clock speed. It is forced to guess the next word within that 30-millisecond window.

This is why it hallucinates — the Time Hierarchy dictates that the problem requires more time, but the server infrastructure cuts it off to save money.

Chapter 02

The "Butter" Budget

The Physical Resource Slice

If we treat "butter" as the physical energy, computing power, and hardware lubrication required to run the trick, the consumption per query is remarkably high.

The VRAM Slice

140–300 GBVRAM monopolised

To process your question, your text inputs are loaded into the high-speed memory (VRAM) of an array of enterprise graphics cards (like Nvidia H100s). For those few seconds, your query completely monopolises this resource depending on the size of the model.

The Electricity Cost

0.0003–0.003kWh per query

A single standard AI query consumes roughly this much electricity. Enough to illuminate a standard LED lightbulb for about 20 minutes, burned in a matter of seconds just to predict your text pattern.

The Water/Cooling Friction

~50 mlwater evaporated

Data centres use water to cool the intense heat generated by these calculations. A single 20-word prompt conversation effectively evaporates about 50ml of water — roughly a standard shot glass — in server cooling towers to keep the hardware from melting.

Chapter 03

The Financial Enclosure

Tech companies strictly restrict this "time and butter" because compute time equals direct financial loss.

Raw Infrastructure Cost

It costs a hyperscaler roughly $0.001 to $0.01 in raw infrastructure costs to calculate a single complex response.

Simultaneous Scale

Because millions of people are querying these systems simultaneously, companies use hidden algorithmic throttles to manage the load.

Silent Compression

If your query is too long, they silently compress your text (truncating the context window) to shave off a few milliseconds of processing time, actively degrading the quality of your answer to protect their profit margins.

The machine gives you exactly enough "time" to formulate a plausible grammatical sentence, and handles just enough "butter" to keep the server farms from catching fire.

Interactive Tool

AI Butter Calculator

Enter your model's operating parameters. Receive a scientifically questionable estimate of how long your AI remains structurally coherent before slowly dissolving into confident semantic butter.

→ prompt_to_ai

Build me a sophisticated reasoning framework that can handle ambiguity, integrate multi-source evidence, and produce reliable architectural decisions under uncertainty. Make it poetic if possible.

0.7
0.0 — frozen, factual, joyless2.0 — creative, molten, ungovernable
18 turns
1 turn — tightly grounded50 turns — recursive semantic soup

Low — harmless autocomplete

Medium — can mutate things

High — may rewrite reality while sounding optimistic

72%
Low — concrete + verifiableHigh — orchestration fabrics, semantic kernels, governance layers, destiny
3

Examples:

  • • real API
  • • persistent database
  • • execution receipts
  • • rollback capability
  • • human verification
0 — pure vibes10 — annoying but trustworthy

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