Dario Amodei and his co-founders were among the first to document, while at OpenAI, that scaling up AI training smoothly improves results across a range of cognitive tasks.

Judge as of 2022-10-13. True: Amodei and one or more later Anthropic co-founders (e.g. Jared Kaplan, Sam McCandlish, Tom Brown, Jack Clark, Chris Olah) wrote, while at OpenAI, early, widely cited work documenting smooth performance improvements from scaling, such as 'Scaling Laws for Neural Language Models' (Kaplan et al., 2020) and the GPT-3 paper, and this work was among the first such documentation. Mostly true: they wrote such work at OpenAI, but earlier work (e.g. Hestness et al. 2017 at Baidu) documented scaling trends, making 'among the first' a modest stretch, or the documented improvements were in loss rather than broadly 'across the board' cognitive tasks. Mixed: they contributed key work, but substantial earlier documentation by others makes the priority claim doubtful. Mostly false: the co-founders' scaling work was not done at OpenAI, or their involvement was marginal. False: they did not write such work.

Set before the research began.

1. E1 Scaling laws for neural language models - OpenAI OpenAI · 23 January 2020

   “Jared Kaplan, Sam McCandlish, Tom Henighan, Tom Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei”

2. E2 Scaling laws for neural language models - OpenAI OpenAI · 23 January 2020

   “The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.”

OpenAI's own page for 'Scaling Laws for Neural Language Models' (2020-01-23) lists Dario Amodei as an author. It also lists later Anthropic co-founders Jared Kaplan, Sam McCandlish and Tom Brown, which shows they did scaling work together at OpenAI (E1). The paper's abstract describes how loss changes smoothly as a power law with model size, data and compute (E2). That is a measure of loss, not results "across the board" on a range of cognitive tasks. Under the criteria, findings about loss rather than broad task performance make the claim a modest stretch, and the evidence does not settle the "among the first" point either way.