Academic research is far behind the AI frontier, in every field we checked
A paper about a model is out of date before it clears review. In mathematics, the newest results come from a model no academic can run.
We bring the tools and techniques software developers already use to finance research, and track the papers, vendors and policies changing the field.
UpdatedA paper about a model is out of date before it clears review. In mathematics, the newest results come from a model no academic can run.
The results come from an internal model. OpenAI says the average result used compute equal to roughly three hours of ChatGPT Pro thinking.
FactorBench compares roughly five thousand machine-mined factors across five equity markets, from genetic programming to LLM agents.
Across 18 long-horizon alpha-research runs and 48 continuation branches, evolved capabilities did not reliably beat the starting set.
Across thirteen text measures on S&P 500 transcripts, the choice of model changes the size, sign and significance of downstream coefficients.
A test on economics papers that use rainfall as an instrument finds accuracy falls as the reading task needs more context.
A study of 15 million Gemini interactions, over 2,600 specialized models and a survey of over 600 scientists maps how AI is used in research.
The choice that matters is not which editor. It is whether you write the code or describe the outcome and review what an agent did.
Automation starts with the stage that has the clearest pass or fail. Turn the data pull into one command with checks and a manifest.
Use the lowest-level, most structured interface the job allows. What that means for data pulls, vendor platforms and software with no API.
Machine learning, mathematics, biomedicine and finance each have a system that runs most of the research loop. Two of them you can install today.