Too Big to Fail?

 

An argument for justifying the use of LLMs is that, if the context of the text is shrunk so each word has only one meaning, the hallucination problem disappears. Then the text would be just like a program.

The problem is:

1.    Words can have multiple Parts of Speech.

“on” can be a preposition or an adverb:

 The car turned on a dime. (preposition)

 He turned on the light. (adverb)

“bar” can be a noun, a verb, a preposition

As a noun, “bar” has about 20 meanings, including some figurative and collective ones:

“Using far ultraviolet, TSMC raised the bar on semiconductor track widths.” The bar on a high-jump frame is being used figuratively – raising the bar makes it harder to jump over, or compete with TSMC.

It can be hard to tell the difference between a figurative use and a literal use:

“Fred raised the bar on forever chemicals in the drinking water at the next meeting, saying the benefits don’t justify the costs.” Bar is being used as a synonym for ban.

2.    Groups of words can have entirely different meanings:

“Fight with”

“He fought with his neighbour”

Sense 0: fight in opposition to something or someone

“He fought with his friends in the Vietnam war”

Sense 1 fight alongside (someone) as allies

The context controls the meaning – trying to restrict the context is a losing battle.

3.    Elision

We watched a movie set in Hawaii

This doesn’t mean that a movie set needed watching – if we put back the elided words, we get:

A movie that is set in Hawaii.

The words can be left out because the meaning is obvious – unfortunately there is nothing in an LLM to put back the elided words or detect an obvious meaning.

What Can an LLM Do?

Iy can work with symbols that have one meaning – about 12% of English words have a single meaning (but common words like “set”, “run”, “on” have many meanings).

What Can’t an LLM Do?

Everything else.

It can’t understand simple text, let alone complex text.

It can’t create mental objects and reason about them – that means it can’t play checkers or chess, it can’t handle interactions between two or more people or objects.

The State of the Market

Literally, trillions of dollars are being poured into infrastructure for LLMs – data centres, Nvidia Chips – by companies like OpenAI, Google, Microsoft, Meta, Apple. It is made worse by the US President spruiking LLMs as American AI to other countries (Saudi Arabia, UAE, Qatar), and them investing billions in it, and  the US Government becoming involved in Nvidia’s revenue from China for chips that were previously banned – in other words, if it fails, the effect will be far wider than the overenthusiasm of a few large tech companies.

There may be a market for having a chatbot chat to someone as a companion, although given the percentage of people suffering from mental health problems in the USA (4% for psychosis, 3.8% for mania), this is fraught with problems (at least three documented suicides). See https://semanticstructure.blogspot.com/2025/11/is-mental-therapy-life-critical-task.html

But all the consultancies are selling it as AI for industry - it must be good!

They see it as money for old rope – they are all doing it so it is no-one’s fault. It might be useful to get some advice from people who know what words mean, and haven’t become bewitched by the ease of selling LLMs to unsophisticated clients.

This post was triggered by putting “too big to fail” in the Semantic AI dictionary. The phrase is being used a lot lately.