There’s a scatterplot that’s been circulating on Twitter. The trend lines show that since the time of GPT-2, open weights models have improved at a steeper rate than proprietary models, with the two on a path to intersect.
I would argue that's to be expected after the first generally accepted POC (GPT-3.5) was released, with it an entire industry created, and other companies actually started copying/competing in a big way.
It seems a stretch to read this as a continuing trend, when (from what I gather everyone agrees on) the way to better models seems to be ever more efficient handling of ever larger amounts of money, compute and data, with no reasonable limits in sight on any of the three.
Scaling up LLMs is only going to go so far, and it will yield diminishing marginal returns on all of that money, compute, and data. It’s a regime of exponential increases in inputs for linear gains in the outputs - barring some technological breakthroughs which could come from anywhere, not just from OpenAI.
It seems a stretch to read this as a continuing trend, when (from what I gather everyone agrees on) the way to better models seems to be ever more efficient handling of ever larger amounts of money, compute and data, with no reasonable limits in sight on any of the three.