About Aaru
Aaru builds simulations of human behavior. Each simulation contains a population of AI agents, each representing a person who could plausibly exist in the real world and capable of making decisions within a modeled environment. Companies and institutions use these simulations to test consequential choices before committing—from product launches and pricing decisions to strategic communications and policy changes.
Building a useful simulation requires more than generating plausible text. Populations must represent real people and groups; predictions must be calibrated; simulations must remain coherent as conditions change; and the product must make the resulting evidence legible enough to support real decisions.
We are a small, in-person team in New York. We work with urgency, high ownership, and intellectual honesty. We expect people to surface inconvenient evidence, change their minds quickly, and carry important work all the way to a result.
About Prediction Research
Prediction Research builds systems that estimate future or otherwise unknown outcomes from data. The team's primary object is the population-level outcome: given a population, a question, and the relevant context, what aggregate result should we expect, how uncertain should we be, and how should that estimate change when the conditions change?
Some problems are best solved with structured statistical or machine-learning methods. Others may benefit from language models, retrieval, tools, explicit decomposition, simulated agents, or a combination of these approaches. The team's job is not to assume that the most complex method is best. It is to determine which information and method produce genuine predictive signal beyond strong, simpler baselines.
Prediction Research is not prompt engineering and it is not a speculative forecasting exercise. It is empirical predictive science. A prediction of 60 percent should resolve near 60 percent under the conditions where it is made. Improvements must survive temporal holdouts, new populations, changing environments, and prospective outcomes.
How we work
We treat prediction as an empirical science. Progress is measured against future or otherwise held-out outcomes, with particular attention to calibration, behavioral shift, subgroup performance, data leakage, and the cost of different errors. Strong baselines matter, including simple historical rates and conventional statistical models.
Research is exploratory, but it must eventually change what Aaru can build or what the company believes. Papers, benchmarks, and prototypes can be valuable along the way. The central goal is to produce predictive systems that remain useful when they encounter new data, new customers, and real consequences.
Management in this function combines scientific judgment with direct people leadership. The manager should create clarity about which uncertainties matter, provide researchers enough context to own them independently, and preserve the discipline to publish nulls, revise methods, or stop work when the evidence demands it.