> Funny. GPT-5 will confidently tell you that Willian H. Brusen is a former US president Checking the link, it seems this mistake occurred when generating an i

> Funny. GPT-5 will confidently tell you that Willian H. Brusen is a former US president Checking the link, it seems this mistake occurred when generating an image of the names of all the presidents, I believe using gpt-image-1 (the model which GPT-5 will call, but which predates GPT-5). Not to say inaccuracies in generated image text shouldn't also be addressed, but I feel it's relevant context when judging progress. > The verdict of some users is in. They hate GPT-5. [...] OpenAI to bring back the older, but more reliable, GPT4o model GPT-5 is top of https://livebench.ai/#/ https://livebench.ai/#/ and top of https://lmarena.ai/leaderboard https://lmarena.ai/leaderboard (blind user-preference ratings). Those leaderboards aren't the be-all-and-end-all, but I feel narratives formed within particular groups can be prone to confirmation bias (with different groups having different narratives and seeing the others as delusional). > and an Arizona State University study indicate that our current ways of improving LLMs have gone as far as they can go Unclear to me that a study of a 4-layer GPT-2 on a synthetic task implies specifically now is the stopping point. I don't believe in a singularity/intelligence explosion or that we're close to replacing all human labor, but it does seem that incremental progress has continued through over a decade of people saying deep learning is hitting a wall. > Kilo Code blog observed, people have been assuming that since "the raw inference costs were coming down fast, the applications' inference costs would come down fast as well but this assumption was wrong." > [...] Of course, Sam Altman, OpenAI's CEO, can predict "The cost to use a given level of AI falls about 10x every 12 months," but I believe that just as much as I would an Old West snake oil huckster who'd guaranteed me his miracle elixir would grow my hair back. Wouldn't necessarily trust Altman's predictions in general, but the fact that the cost of a fixed level of capability decreases dramatically over time seems fairly uncontroversial and easy to verify across providers. That's not inconsistent with the fact that the per-token price of the current best model has stayed approximately the same, which is what the quoted blog post is referring to. I believe that's mostly just determined by how much people are willing to pay (if people are willing to pay more to be a couple months ahead of the current progress curve, scale up the model or increase context/CoT to squeeze out extra performance).