That's baloney. The old ML adage "there's no data like more data" is as old as mankind itself.
Last week Hugging Face released SmolLM v2 1.7B trained on 11T tokens, 3 orders of magnitude more training data for the same number of tokens with almost the same architecture.
So even back in 2019 we can say we were working with a tiny amount of data compared to what is routine now.
The same has always been true. There has never been a stance along the lines of "ah, let's not collect more data - it's not worth it!". It's always been other reasons, typically the lack of resources.
TinyLlama[1] has been made by an individual on their own last year, training a 1.1B model on 3T tokens with just 16 A100-40G GPUs in 90 days. It was definitely within reach of any funded org in 2019.
In 2022 (IIRC), Google released the Chinchilla paper about the compute-optimal amount of data to train a given model, for a 1B model, the value was determined to be 20B tokens, which again is 3 orders of magnitude below the current state of the art for the same class of model.
Until very recently (the first llama paper IIRC, and people noticing that the 7B model showed no sign of saturation during its already very long training) the ML community vastly underestimated the amount of training data that was needed to make a LLM perform at its potential.