News
Papers, releases and policy changes in AI for finance research, each with a TL;DR and the reason it matters to your work.
A new frontier model ships every 10 days, and no university is built to keep up
Anthropic, OpenAI and Google released at least 28 language models between 1 January and 9 October 2026. Higher education staff name the pace of change as their top AI challenge.
AACSB's 2026 standards never mention AI. That is a gigantic mistake.
Its own research says 97 percent of schools already cite AI in accreditation materials and nearly half lack the governance to manage it. The standards are silent.
The standalone academic data platform is running out of reasons to exist
AI assistants now ship with vendor data inside and recognize the licenses a user already holds. The query screen is the part of the platform that does not survive.
Academic research is far behind the AI frontier, from finance to mathematics
A paper about a model is out of date before it clears review.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.
AI has arrived for finance research, and data vendors cannot police it
Agents now operate a computer better than the human baseline on the standard test. Licenses forbid most AI use of data, and almost none of it can be detected.
AI is obliterating the pipeline from finance programs to industry jobs
Data vendors and AI companies now sell to banks and asset managers directly. The junior tasks that absorbed finance graduates are the first ones their products automate.
Researchers in China, Japan and Korea run whole papers through coding agents and post how
Their own forums and blogs describe pipelines of 30 agents, a seven-week paper with 108 experiment runs, and an eight-hour draft. Every account keeps the checking for a person.
If the platform forbids agents, build the dataset yourself from the source
Filings, financial statements and earnings calls were public before any vendor packaged them. An agent can go back to the source, and the tools to do it are free.
Developers are leaving Copilot and editor plugins for Claude Code and Codex
In six months Claude Code went from 18% to 39% of professional developers and Codex from 3% to 16%, while Copilot and Cursor fell. Price, tools and computer use explain it.
Keep paying for call transcripts, but only for the history, the identifiers and the rights
An agent now does the cleaning, splitting and scoring that used to justify a data budget. What it cannot produce is history, identifiers and the right to use the text.
Researchers are using AI against the rules, because being left behind is worse
At a major 2026 conference, more than one in five reviewers who were told not to use an LLM used one anyway. The fear driving that is rational.
OpenAI releases machine-produced math results with Lean proofs and compute figures
The results come from an internal model. OpenAI says the average result used compute equal to roughly three hours of ChatGPT Pro thinking.
A benchmark of nine factor-mining methods finds no approach consistently wins
FactorBench compares roughly five thousand machine-mined factors across five equity markets, from genetic programming to LLM agents.
Self-evolving research agents show no consistent gain from accumulated skills in a factor test
Across 18 long-horizon alpha-research runs and 48 continuation branches, evolved capabilities did not reliably beat the starting set.
Seven LLMs scoring the same earnings calls agree with a rank correlation of 0.52
Across thirteen text measures on S&P 500 transcripts, the choice of model changes the size, sign and significance of downstream coefficients.
LLM literature reviews hold up for broad claims and break down for paper-level ones
A test on economics papers that use rainfall as an instrument finds accuracy falls as the reading task needs more context.
Agents can propose investment factors, but a frozen referee has to judge them
A working paper splits factor research in two and finds that a referee the agent cannot touch admits 5 to 11 times fewer false factors.
Scientists report saving nearly 7 hours a week with AI, and the bottleneck moves to verification
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.
Daiwa Securities moves its Sakana AI consulting platform from trials into production development
Sakana AI, the Tokyo lab behind The AI Scientist, is building a total-asset consulting system for Daiwa under a partnership signed in October 2025.
AACSB's AI framework for business schools grows to 84 schools in its July 2026 update
The report series went from 26 institutions in July 2025 to 48 in January 2026 and 84 in July, and it names faculty development as the critical success factor.