The mistakes it makes depends on the model and the language. GPT5 models can make horrific mistakes though where it randomly removes huge swaths of code for no reason. Every time it happens I'm like, "what the actual fuck?" Undoing the last change and trying usually fixes it though 🤷

They all make horrific security mistakes quite often. Though, that's probably because they're trained on human code that is *also" chock full of security mistakes (former security consultant, so I'm super biased on that front haha).

I think that people are too enthralled with the current situation that's centered around LLMs, the massive capital bubble and the secondary effects from the expansion of datacenter space (power, water, etc).

You're right that they do allow for the disruption of labor markets in fields that were not expecting computers to be able to do their job (to be fair to them, humanity has spent hundreds of millions of dollars designing various language processing software and been unable to engineer the software to do it effectively).

I think that usually when people say 'AI' they mean ChatGPT or LLMs in general. The reason that LLMs are big is because neural networks require a huge amount of data to train and the largest data repository that we have (the Internet) is text, images and video... so it makes sense that the first impressive models were trained on text and images/video.

The field of robotics hasn't had access to a large public dataset to train large models on, so we don't see large robotics models but they're coming. You can already see it, compare robotic motion 4 years ago using a human engineered feedback control loop... the motions are accurate but they're jerky and mechanical. Now look at the same company making a robot that uses a neural network trained on human kinematic data, that motion looks so natural that it breaks through the uncanny valley to me.

This is just one company generating data using human models (which is very expensive) but this is the kind of thing that will be ubiquitous and cheap given enough time.

This isn't to mention the AlphaFold AI which learned how to fold proteins better than anything human engineered. Then, using a diffusion model (the same kind used in making pictures of shrimp jesus) another group was able to generate the RNA which would manufacture new novel proteins that fit a specific receptor. Proteins are important because essentially every kind of medication that we use has to interact with a protein-based receptor and the ability to create, visualize and test custom proteins in addition to the ability to write arbitrary mRNA (see, the mRNA COVID vaccine) is huge for computational protein design (responsible for the AIDS vaccines).

LLMs and the capitalist bubble surrounding them is certainly an important topic, framing it as being 'against AI' creates an impression that AI technology has nothing positive to offer. This reduces the amount of people who study the topic or major in it in college. So in 10 years, we'll have less machine learning specialists than other countries who are not drowning in this 'AI bad' meme.