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I think this is a pretty big deal and disagree with posters here who are saying it’s a nothingburger. For the kinds of tasks people here are currently using ChatGPT for, the extra robustness and reliability you get with 1o may not be necessary. But the real transformative use cases of LLMs are going to be when they take on a more agential role and can carry out extended complex actions, and this kind of “unhobbling” (as Aschenbrenner puts it) will be essential there.
For some accessible background on why increasing inference-cost : training-cost ratio may be the key to near-term task-specific superintelligence, I recommend this brief blogpost from a couple of months ago. Ethan Mollick also has some initial hands-on thoughts about 1o that might be of interest/use.
The default failure-state for emerging tech is that a relative lack of improvement is explained away as setting the stage for a future explosion of progress. In a very few instances this has been true, but overwhelmingly it's been false, e.g. crypto, the metaverse, VR, etc. At best, LLMs are currently on the downswing of the Gartner Hype Cycle.
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