Especially relevant today with the release of grokipedia

In high school debate, I found a killer piece of evidence to counter any given doomsday argument.

From Eric Zencey:

There is seduction in apocalyptic thinking. If one lives in the Last Days, one’s actions, one’s very life, take on historical meaning and no small measure of poignance.

I continue to be surprised that LLM providers haven't been legally cudgeled into neutering the models from ever giving anything that can be construed as medical advice.

I'm glad--I think LLMs are looking quite promising for medical use cases. I'm just genuinely surprised there's not been some big lawsuit yet over it providing some advice that leads to some negative outcome (whether due to hallucinations, the user leaving out key context, or something else).

What do you mean? A single data point cannot be exponential. What the blog post say is that the ability to solve tasks of all LLMs is exponential over time, and GPT-5 fits in that curve.

That SWE-bench chart with the mismatched bars (52.8% somehow appearing larger than 69.1%) was emblematic of the entire presentation - rushed and underwhelming. It's the kind of error that would get flagged in any internal review, yet here it is in a billion-dollar product launch. Combined with the Bernoulli effect demo confidently explaining how airplane wings work incorrectly (the equal transit time fallacy that NASA explicitly debunks), it doesn't inspire confidence in either the model's capabilities or OpenAI's quality control.

The actual benchmark improvements are marginal at best - we're talking single-digit percentage gains over o3 on most metrics, which hardly justifies a major version bump. What we're seeing looks more like the plateau of an S-curve than a breakthrough. The pricing is competitive ($1.25/1M input tokens vs Claude's $15), but that's about optimization and economics, not the fundamental leap forward that "GPT-5" implies. Even their "unified system" turns out to be multiple models with a router, essentially admitting that the end-to-end training approach has hit diminishing returns.

The irony is that while OpenAI maintains their secretive culture (remember when they claimed o1 used tree search instead of RL?), their competitors are catching up or surpassing them. Claude has been consistently better for coding tasks, Gemini 2.5 Pro has more recent training data, and everyone seems to be converging on similar performance levels. This launch feels less like a victory lap and more like OpenAI trying to maintain relevance while the rest of the field has caught up. Looking forward to seeing what Gemini 3.0 brings to the table.

Watching the livestream now, the improvement over their current models on the benchmarks is very small. I know they seemed to be trying to temper our expectations leading up to this, but this is much less improvement than I was expecting