«We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.» [0] It is not that access to all information was hidden to anyone before, it was harder to find it, sure. Now it can be found easily and aggregated, however, it is not as simple as saying "Do X" and believe that X is correct. Recall the infamous Swing & Product Management meme? [1] The problem is the absence of information you provide, the LLM will have to make a guess of what you mean, much like the human. Likelihoods of this guess being correct depends on the domain and background. So yes, it'll likely build whatever you ask it (I do think we're there already), but if that matches what you intended to actually get is vastly different. You'll be settling with "Good enough" though and declare it working.
Others already pointed out that 6 months seem to be the constant of distance, irrespective of the date.
[0] - https://news.ycombinator.com/item?id=50063427#50063744
[1] - https://www.productftw.com/productftw-2-the-tire-swing-carto...
https://waymo.com/blog/2025/10/creating-an-all-weather-drive...
The corpus of code available to train LLMs on is rich in both volume and quality. The same can't be said for LLM prompts. Prompts only came into existence alongside LLMs themselves, so most of the prompts out there are actually LLM generated, and training on them causes every model to overfit to the same patterns.
LLMs can give clear and concise directives to other LLMs, but only for basic tasks. Beyond that, they frequently overstuff their prompts with too much prose and too much direction, and worse, they often fill them with subtle contradictions.
Of course, there's a spectrum here. Some models turn out to be better at this than others, and I suspect this is what differentiates Fable-type models from the rest, and why vendors have started keeping their models' reasoning confidential.
Ten years ago, in France, two doctors (one in a professional setting and the other during a private consultation) asked me about a wound I had on my shoulder; obviously I couldn't identify what it was, and they offered no suggestions, so I supposed it was not something important.
It took a visit to a cardiologist—for an entirely different reason—for him to suggest that I rapidly consult a dermatologist.
It was a carcinoma, and surgery removed it. If ChatGPT had existed back then, this carcinoma would have been removed several years earlier.
Current diagnostic setup relies on humans having vast expert knowledge, when in fact it's almost always impossible.
A well trained neural net/LLM will always be better at visual detection because of sheer number of examples it sees during training, compared to a dermatologist which, if lucky will observe no more than 20 patients/day x 270 days a year x 40 years of practice i.e. around 220k samples throughout his lifetime.
MD with other specialties will see significantly less and we are talking about top of the top specialists.
An average MD knows more amount medical diagnostics than than the average Joe, but in principle much less than a well trained AI.
Do we have those kinds of models already? Maybe not yet but, ceteris paribus, we will definitely have them in the future.
IMHO we do have those models already but they aren't available to the public, though their availability would certainly lower cost for healthcare providers.
Automation doesn't make up for carelessness.
Reasonable treatments for small, non-aggressive skin cancers (or suspected cancers) are extremely low risk. And reasonable practitioners will often suggest that you merely keep an eye on an abnormal spot - because it doesn't look worth the bother of any sort of treatment for now.
But if we think a little further and generalize then there is great risk (and by extension, harm) to delegate diagnosis and treatment (of health issues in general, including potentially or supposedly fatal ones) to ChatGPT and similar tech. That we'd end up with a net positive is far from obvious.
Conversely, ChatGPT can help avoid false negatives.