Considering this is from academia, there's a chance there were limitations on the available models. My research group accesses OpenAI models via Azure, and until recently (last week) the latest model was GPT 5. We just got 5.4.

There is now a trillion-dollar industry bent to the task of convincing people these things can think. It’s gonna cause some damage.

…but they reason well enough given enough context (using their matmuls).

There is a movie, Gold (2016), about a fake gold mine. One of its founders is a true believer: he found a few chunks of gold and started digging for more. The other founder is a nihilist: he realised that there is no gold there, but who cares if he makes the investors believe? So he does, and almost sells the company for $300M.

In our story, investors are mining intelligence from GPUs, and they truly believe they are one inch from discovering the biggest goldmine in history. But GPUs, unlike a goldmine, cannot be inspected for traces of gold by independent contractors. To keep the hype up, the nihilists in our story dig up cheap gold-looking metals from time to time and tell investors that with a bit of alchemy - agentic workflows, etc. - those metals can be magically turned into gold.

Investors will keep digging until the end of the age, or until they run out of money.

I disagree, their findings should generalize to the frontier. Even if the latest can deal with the extra complexity, it stands to reason it will take more tokens to do less. This could be a useful insight into the next generation of evals.