Deepseek recently published a paper in which they describe that vision tokens contain more information than text tokens and that this can be used to compress context.

We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping.

Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10×), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20×, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs.

It reminds me of LLM caveman speak, it used to have another option to use Chinese instead of English. A language like Chinese is seemingly better at encoding information in fewer tokens and I think this is the same mechanism why OCR tokens work so well.

That said, I also doubt that voice messages are more efficient than text prompts, but it's best not to waste too much time engaging with these sorts of LinkedIn posts (and LinkedIn in general).

LLMs don't need accuracy. This just boils down to speaking being faster than typing, especially if your thought isn't fully formulated.