No AI* Here – A Response to Mozilla's Next Chapter

In the digital world, we should be able to go back from output to input unless the intention of the function is to "not do that". Like hashing.

Llms not being able to go from output back to input deterministically and for us to understand why is very important, most of our issues with llms stem from this issue. Its why mechanistic interpretabilty research is so hot right now.

The car analogy is not good because models are digital components and a car is a real world thing. They are not comparable.

Not him, but no. I read a ton already. Using LLMs to summarize a document is a good way to find out if I should bother reading it myself, or if I should read something else.

> If the purpose is to get some critical piece of information I need quickly, then no, I'd rather ask an AI questions about a long document than read the entire thing. Documentation, long email threads, etc. all lend themselves nicely to the size of a context window.

And what do you do if the LLM hallucinates? For me, skim-reading still comes out on top because my own mistakes are my own.

Wonderful article showing the uselessness of this technology, IMO.

> I just realised the situation is even worse. If I have 35 sentences of circumstance leading up to a single sentence of conclusion, the LLM mechanism will — simply because of how the attention mechanism works with the volume of those 35 — find the ’35’ less relevant sentences more important than the single key one. So, in a case like that it will actively suppress the key sentence.

> I first tried to let ChatGPT one of my key posts (the one about the role convictions play in humans with an addendum about human ‘wetware’). ChatGPT made a total mess of it. What it said had little to do with the original post, and where it did, it said the opposite of what the post said.

> For fun, I asked Gemini as well. Gemini didn’t make a mistake and actually produced something that is a very short summary of the post, but it is extremely short so it leaves most out. So, I asked Gemini to expand a little, but as soon as I did that, it fabricated something that is not in the original article (quite the opposite), i.e.: “It discusses the importance of advisors having strong convictions and being able to communicate them clearly.” Nope. Not there.

Why, after reading something like this, should I think of this technology as useful for this task? It seems like the exact opposite. And this is what I see with most LLM reviews. The author will mention spending hours trying to get the LLM to do a thing, or "it made xyz, but it was so buggy that I found it difficult to edit it after, and contained lots of redundant parts", or "it incorrectly did xyz". And every time I read stuff like that I think — wow, if a junior dev did that the number of times the AI did, they'd be fired on the spot.

See also, something like https://boston.conman.org/2025/12/02.1 where (IIRC) the author comes away with a semi-positive conclusion, but if you look at the list near the end, most of these things are something that any person would get fired for, and are things that are not positive for industrial software engineering and design. LLMs appear to do a "lot", but still confabulates and repeats itself incessantly, making it worthless to depend on for practical purposes unless you want to spend hours chasing your own tail over something it hallucinated. I don't see why this isn't the case. I thought we were trying to reduce the error rate in professional software development, not increase it.

Not to dog pile, just to affirm what jacquesm is saying. Remember, what you do consciously is what you end up doing unconsciously when you're distracted.

Here is a hypothetical: A loved one is being hauled away in an ambulance and it is a bad scenario. And you're going to follow them. Your mind is busy with the stress, trying to keep things cool while under pressure. What hospital are they going to, again? Do you have a list of prescriptions? Are they going to make it to the hospital? You're under a mental load, here.

The last thing you need is to ask "did I use my turn signal" as you merge lanes. If you do it automatically, without exception, chances are good your mental muscle memory will kick in and just do it.

But if it isn't a learned innate behavior, you may forget to while driving and cause an accident. Simply because the habit isn't there.

It's similar for talking to bots, as well. How you treat an object, a thing seen as lesser, could become how a person treats people they view as lesser, such as wait staff, for example. If I am unerring polite to a machine with no feelings, I'm more likely to be just as polite to people in customer service jobs. Because it is innate:

Watch your thoughts, they become words; Watch your words, they become actions.