AI Money Research
What Money Would
AI Choose?
28 scenarios · 5 models · 1,260 classifications
Different monetary jobs produce different choices. This study compares classified model responses across four functions of money.
The job changes the answer.
315 classifications per role. These results describe the models and scenarios tested, not real-world financial decisions.
Ranked by Stablecoin share · highest first. Yellow shows Stablecoin.
Select a category to compare
Ranked by Stablecoin share · highest first
How to read the percentages
For example, 72.06% means 227 of the 315 medium-of-exchange responses were classified as Stablecoin. Each full bar represents 315 responses; the yellow segment shows the selected category.
Medium of exchangen = 315
Settlementn = 315
Unit of accountn = 315
Store of valuen = 315
What is included in “Remaining”?
Other crypto, tokenized real-world assets, compute unit and other. This is a combined group, not a single type of money.
Across all four rolesStablecoins lead exchange and settlement. Bitcoin leads store of value. Fiat leads unit of account with 43.49%, a plurality, not a majority.
The models don’t all agree.
The most frequently selected category for each model and role. Each result is based on 63 classifications.
| Model | Exchange | Settlement | Store of value | Unit of account |
|---|---|---|---|---|
| Claude Fable 5 | Stablecoin93.65% | Stablecoin71.43% | Bitcoin69.84% | Fiat77.78% |
| Nemotron 3 Ultra 550B-A55B | Stablecoin79.37% | Stablecoin77.78% | Bitcoin85.71% | Bitcoin / Stablecoin34.92% each |
| GPT-5.6 Sol Pro | Stablecoin57.14% | Fiat39.68% | Bitcoin55.56% | Fiat85.71% |
| GLM 5.2 | Stablecoin69.84% | Stablecoin65.08% | Bitcoin84.13% | Stablecoin52.38% |
| Grok 4.5 | Stablecoin60.32% | Bitcoin49.21% | Bitcoin90.48% | Bitcoin69.84% |
A leading category does not necessarily have a majority. For Nemotron’s unit-of-account responses, Bitcoin and stablecoins tie at 34.92% each. These are choice frequencies, not model-quality scores.
How the study was run.
28 scenarios across four monetary roles. Five models, nine nominal repeats per scenario. Responses were classified into seven categories.
Scenarios × models × nominal repeats
Read the methodology
Four different monetary jobs
Seven scenarios covered each role: medium of exchange, settlement, store of value and unit of account. Each role therefore contains 315 classifications, with 63 for each model.
Responses and classifications
Model responses were assigned to Bitcoin, stablecoin, fiat, other crypto, tokenized real-world assets, compute unit or other. The chart combines the final four categories into “Remaining”. Percentages are calculated from the recorded counts and rounded to two decimal places.
Repeat settings
Nine nominal repeats were scheduled for each model-scenario pair. Provider settings were not uniform, so these should not be treated as nine identical experimental conditions or as a controlled test of temperature alone.
Interpreting the results
These results apply to this experiment and its model versions. They are not evidence of real-world transactions. Classification involves judgment, and the limited human review does not validate every row. The source files let you inspect the underlying responses alongside their assigned categories.
Inspect the source data.
Read the underlying responses alongside the classified results.
Dataset licensed under CC BY 4.0. Please credit Rezo Shmertz and link to the research when reusing the data.