Nof1 is an AI research lab building new models for financial markets.

We believe markets are the next great domain for frontier AI research. They are open-ended, deeply consequential, and highly measurable.

Why markets

The current generation of LLMs are shockingly bad at navigating financial markets. They’ve read vast amounts of financial text, but struggle with even the simplest investing tasks, showing minimal signs of improvement between releases.

Part of the problem is that markets are non-stationary. Things that worked well in the past most likely won’t work in the future. This does not fit into the current training paradigms used by frontier AI labs, despite working for domains like coding, math, and law. To train frontier models for markets, we need new techniques that can handle noisy, constantly changing environments. It’s in these environments where the most interesting, economically relevant problems live.

New capabilities

At the same time, the field of AI research is running out of good ways to measure AI progress. New benchmarks get quickly memorized and saturated, specifically because they don’t adapt alongside the models. This all leads to an interesting question: what if markets are a solution? What if we can use them as both an evolving benchmark and a training environment for new capabilities?

This would directly address one of the biggest failure modes of modern LLMs: adaptability. Current models are overly static and backwards-looking, struggling to update or think ahead. Markets provide a natural setting for developing this capability, exposing weaknesses that static benchmarks often miss. They also provide something increasingly valuable for training AI systems: objective feedback.

  1. Earnings beat consensus and management raises full-year guidance
  2. A surprise CPI print
  3. Quarter-end rebalancing flows push against the rally
  4. Price breaks above its twelve-month high on rising volume
  5. Fed decides to raise interest rates
  6. The regime shifts to risk-off

Our models

We’re building new models for financial markets, alongside the environments and benchmarks needed to train and evaluate them. Our training objective is general market intelligence, something we’re constantly improving our measure of internally. We’ve identified a proto scaling law for financial intelligence, with signs of skill-transfer to other domains.

Upon release, our models will be able to perform well-calibrated research, analyze large time-series datasets, do complex feature engineering, write market-aware code, make forecasts, and improve based on the outcomes of their decisions. An adaptive reasoner that can be used in any harness by hedge funds, individuals, aggregators, brokerages, and financial institutions.

  • Forecasting
  • Risk management
  • Portfolio management
  • Systematic trading
  • Backtesting & simulation
  • Execution

Team

Our team’s work spans across open-ended learning, world modeling, and large-scale RL, with alumni from DeepMind, Two Sigma, Citadel, Bridgewater, and Netflix. Research is led by Julian Togelius, the founder of NYU’s Open-ended Learning Lab, with 20+ years in frontier AI research.

We’re backed by Village Global and leading AI researchers & founders including Tim Rocktäschel (DeepMind), Jack Parker-Holder (DeepMind), Oliver Cameron (Odyssey ML), alongside angels from Jane Street.