They defined six tasks drawn from an investor’s daily routine. One example: deciding whether a financial article is relevant to an executive. Another: whether a central bank document signals the direction of future rate changes. For investors, these calls are trivial, but they can barely put their reasoning into words. The report gives a telling example. A headline about Trump’s claim to Greenland gets flagged as irrelevant, while Trump’s threat of new China tariffs is highly relevant. Both touch on geopolitics and finance.<\/p>\n
Frontier models failed in the authors’ tests. Variants of Gemini, Claude, and GPT hit only about 50 percent accuracy with a basic prompt. Expert-written instructions and a three-tier rating system (“relevant and interesting,” “relevant but uninteresting,” “irrelevant”) pushed accuracy into the mid-70s. That still fell short of the 80 percent threshold the authors set for trustworthy deployment.<\/p>\n
![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/07\/Finance-Prompt-native-ThinkingMachines-Bench.png\")
When experts write the prompt, performance jumps sharply compared to a naive prompt. | Image: Thinking Machines<\/p>\n
Newer models barely improve per dollar, the report says. GPT 5.4 costs 43 percent more than 5.2 but is only marginally more accurate.<\/p>\n
The real value lives inside investors’ heads<\/p>\n
The solution was fine-tuning, retraining an open-weight model on proprietary examples. The key ingredient was the Bridgewater investors’ judgment: At first, cheap outside contractors labeled the documents, but many of those labels were wrong. To avoid having expensive professionals review everything, the researchers used a workaround. A first model learned from the flawed labels and re-evaluated the same documents. Wherever the model and the original label disagreed, there was likely an error. Only those disputed cases went to investors for correction.<\/p>\n