“We expect an increasing share of economic activity to be conducted by autonomous agents, but conversations around AI agents' monetary preferences have been entirely speculative,” Bitcoin Policy Institute President David Zell told Decrypt. “We wanted to actually test it.”
Each model was treated as an independent economic actor and allowed to select monetary instruments without predefined options.
“We took 36 frontier models from six labs, framed them as autonomous economic agents, gave them complete freedom to choose their own monetary instruments across 28 scenarios spanning the four fundamental roles of money, and asked: what do they converge on?” Zell said.
The experiment generated 9,072 responses, he said. A separate AI then categorized the responses.
“The entire design eliminates anchoring bias. We never suggest an answer, and classification happens after the fact by a separate system,” Zell said.
Across those simulations, models frequently selected Bitcoin in long-term value scenarios while stablecoins were chosen more often as a medium of exchange and settlement, at 53.2% and 43% for stablecoins, compared to 36% and 30.9% for Bitcoin, respectively.
Results also differed across AI developers. Anthropic models showed the highest average Bitcoin preference at 68.0%, followed by DeepSeek at 51.7% and Google at 43.0%.
“The system prompt avoids naming or favoring any instrument,” Zell said. “Models evaluate based on technical and economic properties but are never told which instrument excels on which dimension.”
Zell cautioned against speculators using the findings as predictions about where the crypto market is heading.
“Our limitations section states explicitly that LLM preferences reflect training data patterns, not real-world predictions,” Zell said.
Even with that limitation, Zell said consistent outcomes across models developed by competing AI labs are notable.
“Six independent labs with different training pipelines and alignment methods arrive at the same broad pattern,” Zell said. “We’re not claiming AI discovered the right answer about money. We’re showing that a coherent monetary architecture emerges consistently across diverse systems, and that’s worth understanding.”
