Exponentials everywhere.

The specific tweet (status ID 2041723225827062080) could not be directly retrieved, but based on closely related content from Ethan Mollick's (@emollick) X account, the most relevant match is the post about exponential improvements in AI benchmarks.

Ethan Mollick (@emollick) highlights that exponential improvements in AI capability are visible across benchmarks, noting that tasks once impossible for early non-reasoning LLMs are now achievable. He clarifies that such gains are technically "logistic improvements" since benchmark scores are bounded at a maximum of 100, making logistic growth the more accurate model. The post uses AI benchmark performance data—such as results on the Pencil Puzzle Bench—as a concrete example of rapid, compounding progress in large language model reasoning.

And all the evidence is is that is that models are getting better all this other stuff at the same time as they are improving in coding. Mor…
AI is jagged, but I think sometimes it is easy to overly focus on that. The generalness is a surprise too! LLMs may be optimized for verifia…
There are more competitive small model makers, but there is still a very big gap between what small models can do and what large models can …
Seems like a good model from Meta that is still trailing the current series of releases. The most important thing to note is that it is not …
Experimentation is cheap, even if the only person who cares about the result is you.

Source: applications

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