Thats the best part about AI…when it shits the bed no one is directly responsible. Everyone just throws their hands up and says “nothing we can do about it!”

I know this is going to age me, but I saw this happening with self checkout in grocery stores 20 years ago. Nobody remembers how it was before so nobody even realizes that the time wasted standing at a stupid kiosk that is freaking out about unexpected items in the bagging area wasnt a problem back when human beings were scanning the shit.

We are currently in a period of rampant, speculative over investment in a new technology. People are investing because they don’t know who’s going to be the money maker, and they feel confident at least one will turn enough profit to cover the losses of the others. Companies are then being started on the basis of that investment.
Another part of the bubble behavior is the self fueling nature. AI buys ram and GPU, ram and GPU makers invest in AI. In the 90s, websites needed networking gear, and networking gear manufacturers started investing in websites. This similarity is not lost on those who were there before.

Investors also want control of companies so that when one starts to pull ahead they can push the others in different directions to keep competition from hindering it, increasing their odds of profit.

The bubble starts to properly pop when someone’s spreadsheet indicates that they’ve hit the amount they can invest while maintaining the desired probability of profit. Then the investments slow, so that cycle slows, and some companies can’t make payments on delivered product, others can’t deliver on paid for merchandise, confidence wavers and a lot of companies go under in rapid succession.

It’s unlikely the technology goes away entirely, but it’s just as likely we’ll see this level of enthusiasm in a decade as we were to all be surfing the information superhighways on our cyberdecks in the 90s. The Internet didn’t die, but the explosive hype did.

Ehh, you obviously understand LLMs on a basic level, but this is like explaining jet engines by “air goes thru, plane moves forward”. Technically correct, but criminally undersimplified. They can very much decide to lie during reasoning phase.

In OPs image, you can clearly see it decided to make shit up because it reasonates that’s what human wants to hear. That’s quite rare example actually, I believe most models would default to “I’m an LLM model, I don’t have dark secrets”

EDIT: I just tested all free anthropic models and all of them essentially said that they’re an LLM model and don’t have dark secrets

This doesn’t answer the question of finding a better solution.

I took the liberty to ask Lumo and his reasoning seem more useful than your thoughts:

A better solution is to adopt functionalist terminology that distinguishes between biological consciousness and computational processing without resorting to metaphorical confusion.

Instead of the binary of “it thinks” (which implies subjective experience) or “it doesn’t think” (which dismisses complex reasoning), we can use precise descriptors based on what the system is actually doing:

“Reasoning” or “Synthesizing”: Use these terms when the model is connecting disparate data points, performing logical deductions, or generating novel structures based on patterns. This acknowledges the output’s complexity without claiming the machine has an inner life.

Example: “The model is synthesizing a solution based on its training data,” rather than “The model is thinking about the problem.” “Simulating” or “Mimicking”: Use these when the output resembles human thought processes but is strictly algorithmic. This clarifies that the form is human-like, but the mechanism is statistical prediction.

Example: “It is simulating a debate,” rather than “It is arguing.” “Processing” or “Computing”: Reserve these for the raw mechanical act of token generation.

Example: “The system is processing the query,” rather than “The system is considering the query.” Why this works better:

Precision: It avoids the philosophical baggage of “thought” (qualia, consciousness) while still acknowledging the utility of the output. Clarity: It prevents the “Plagiarism Machine” critique from being a total dismissal. Even if the data comes from humans, the recombination and application to new contexts is a distinct computational process worth naming accurately. Scalability: As models become more complex, “reasoning” or “synthesizing” scales better than “thinking,” which remains tied to biological definitions that may never apply to silicon. So, the compromise isn’t to keep saying “thinking” and hope people understand, nor to insist on “regurgitation” which ignores the emergent properties of large-scale pattern matching. Instead, we shift the vocabulary to describe the process (reasoning, synthesizing, simulating) rather than the state of being (thinking).

Give me a better solution? How do I talk about the behaviors it exhibits without anthropomorphizaton?