Crunch (Quant/acc)

One scientist, trained by thousands.

His models did the talking. The best model for predicting how fat cells respond to a gene switch-off came from a self-taught data scientist. Laurent Renard is a signal processing engineer by training and an actuarial Python developer at BNP Paribas Cardif. In Part 1 of the Broad Institute’s Obesity ML Competition, his model finished first, more than 50% ahead of second place. He also ended 2025 at number one on a DataCrunch leaderboard, Crunch’s weekly ranking of US stocks. In only two years he has gone from 848th to 17th of all time, collecting two gold medals and the Crunch Grandmaster title along the way.

"my model found alpha" your model found a data leak

An institution's problem, a stranger's model, a lab's confirmation.

Crunch cut a tier-one global bank's currency trading costs by 4%. 700 researchers built the models, and the engine turns their forecasts into one price and a confidence score in under 60 microseconds. On a $10 billion-a-day desk, that 4% is about $10 million a year. When the market moves, the price is already there.

Grateful to ADIA Lab for having us in Abu Dhabi this week. Over five months, 1,700+ Crunchers worked on the Structural Break Challenge: Real-Time Edition. The challenge culminated at the ADIA Lab Symposium, where Jean Herelle presented the winning solutions to an audience of the brightest minds in the field, Nobel laureates among them. Twelve of our top Crunchers made the trip, with several presenting their own work. We’re proud of each of them and of the dedication and ingenuity that brought them there.

Your model is your CV, the ranking is your interview, and the pay is onchain.

Horst Simon is one of the people who shaped supercomputing, the field today's AI runs on. His forty years of work span Boeing, NASA and Berkeley Lab, and include two Gordon Bell Prizes and the TOP500 ranking of the world's fastest computers. In Episode 2 of Minds & Machines, he talks with Jean Herelle (@herelle_jean) about AI, supercomputing and the research that ADIA Lab, which he directs, has run on Crunch since 2023.

Why would a Nobel laureate co-design a competition on Crunch? For Guido Imbens, it’s about democratizing the entire scientific process and making it less subjective.

Surrounded by noise. Working on signal.

Mutian Hong is a Crunch Grandmaster, ranked 9th on the all-time leaderboard, and a PhD student at Westlake University researching large language models. As an undergraduate at ShanghaiTech University, he founded and led DataTech, the university's data science and AI club. He competed at the front line while bringing his campus community into competitive data science. Today, ShanghaiTech ranks 4th among all institutions on Crunch. On Crunch, he finished in the top five of two consecutive ADIA Lab challenges. In the Causal Discovery challenge, he and his teammate placed 3rd out of 1,904 participants by feeding the outputs of established causal discovery algorithms into a gradient boosting model as features. In the Structural Break challenge, they placed 5th with a ROC AUC of 89.28%. Their model is now one of the reference models in the Crunch Open Benchmark, the baseline every new participant measures themselves against. Now at Westlake University, he brings the data intuition and empirical rigor he built through competition to his research on large language models.

5,154 authors were listed on the paper that published the mass of the Higgs boson in 2015. Two independent teams at CERN had each measured it on their own, and by pooling their data they arrived at a value more precise than either team could have reached alone. Crunch is built on the same idea. Thousands of data scientists build models for a single problem independently, and we combine the best of them into one ensemble.

AI has nearly run out of internet to read. The International AI Safety Report 2026, chaired by Yoshua Bengio, warns that high-quality language data is finite. As that supply runs out, the most valuable data becomes the reasoning experts carry.

Overfitting a model is like learning a route by the red van parked on the corner. It works until the van moves, and you miss your turn.

Ask 13,000 Crunchers for a random number and you'll get the same answer 13,000 times.

From Nairobi to Tokyo, Montreal to Singapore, and everywhere except Antarctica (for now). In the last 7 days, Crunch received submissions from 150+ cities across six continents. One week of quantitative research, mapped.

Beam lets a bank or a ministry run a frontier model on its own servers, with its own data, under its own rules. At 501B parameters with Apache 2.0 weights, running frontier AI becomes much more of an engineering decision, and much less of a contract negotiation.

James Villa joined Crunch in June 2021, among the first members on the platform. Five years and 18 competitions later he is a Grandmaster, ranked 13th of all time. He runs Chaos Capital Management, his own trading firm, and the competition he won reflects the same discipline: Mid+One, where Crunchers called the direction of a price series 30 steps ahead, with every prediction due within 20 milliseconds and scored on the profit it would have made after trading costs. James works by iteration, and with 1,892 submissions he has made more than any other Cruncher, earning a gold, a silver and four honorable mentions along the way.