Research conducted by Jerry Han at Princeton found that between 2018 and 2024, data improvements accounted for more than three times as much pre-training algorithmic progress as model architecture improvements

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OpenAI researchers in the top 10% of AI tool usage increased their internal coding agent consumption from approximately $1,000 per day to over $7,000 per day between June 2026 and late August 2026

Meter concluded that the majority of Anthropic's AI progress acceleration was attributable to Anthropic's internal use of AI for research and development

Meter concluded that the majority of Anthropic's AI progress acceleration was attributable to Anthropic's extensive use of AI for research and development

Meter is an independent evaluation organization that spent three months embedded inside Anthropic

DeepSeek reported in a research paper that systematic improvements in synthesized data and environments accounted for essentially all post-training capability gains rather than reinforcement learning algorithm modifications

A study co-authored by Dwarkesh Patel and Jerry Hahn found that over a six-year period, pre-training progress derived more than three times more from data improvements than model improvements

The independent evaluation organization Meter spent three months embedded inside Anthropic

DeepSeek reported that improvements in the scale, diversity, and verifiability of synthesized data and environments accounted for essentially all post-training gains in their model

Approximately 80% of algorithmic progress at frontier AI laboratories is driven by their top 50 researchers

Toby Ord published an essay arguing that artificial intelligence systems automate mathematical proofs without demonstrating the ability to pose powerful questions or create new fields of mathematical inquiry

Frontier AI labs structure model development into independent teams specializing in pre-training data filtering, synthetic reinforcement learning environments, and attention kernels

Maximal update parametrization allows AI researchers to predict optimal hyperparameters for large neural network training runs using models that are 100 times smaller

Anthropic reported that Claude led 26% of Anthropic's AI research and development work as of September 2026, up from under 1% in February 2026

Maximal Update Parametrization allows researchers to predict optimal hyperparameters for a target model from a run that is 100 times smaller

DeepMind published early research exploring the use of federated learning techniques to train language models

Professor John Renne extracted geospatial and infrastructure data for over 110,000 global train stations from OpenStreetMap using code generated by ChatGPT

During the 1980s, Exxon scientists conducted internal research on climate change that accurately projected global temperatures for 2020

In an analysis of 757 adults, individuals with higher reasoning test scores exhibited larger cortical volume but lower neurite density and branching complexity

Professor John Renne created an American transit-oriented development database containing 4,500 train stations

Sedimentary environmental DNA has been successfully recovered from geological deposits dating back approximately two million years