Out of the Software Crisis

The LLMentalist Effect: how chat-based Large Language Models replicate the mechanisms of a psychic’s con

By Baldur Bjarnason,

For the past year or so I’ve been spending most of my time researching the use of language and diffusion models in software businesses.

One of the issues in during this research—one that has perplexed me—has been that many people are convinced that language models, or specifically chat-based language models, are intelligent.

But there isn’t any mechanism inherent in large language models (LLMs) that would seem to enable this and, if real, it would be completely unexplained.

LLMs are not brains and do not meaningfully share any of the mechanisms that animals or people use to reason or think.

LLMs are a mathematical model of language tokens. You give a LLM text, and it will give you a mathematically plausible response to that text.

There is no reason to believe that it thinks or reasons—indeed, every AI researcher and vendor to date has repeatedly emphasised that these models don’t think.

There are two possible explanations for this effect:

  1. The tech industry has accidentally invented the initial stages a completely new kind of mind, based on completely unknown principles, using completely unknown processes that have no parallel in the biological world.
  2. The intelligence illusion is in the mind of the user and not in the LLM itself.

Many AI critics, including myself, are firmly in the second camp. It’s why I titled my book on the risks of generative “AI” The Intelligence Illusion.

For the past couple of months, I’ve been working on an idea that I think explains the mechanism of this intelligence illusion.

I now believe that there is even less intelligence and reasoning in these LLMs than I thought before.

Many of the proposed use cases now look like borderline fraudulent pseudoscience to me.

The rise of the mechanical psychic

The intelligence illusion seems to be based on the same mechanism as that of a psychic’s con, often called cold reading. It looks like an accidental automation of the same basic tactic.

By using validation statements, such as sentences that use the Forer effect, the chatbot and the psychic both give the impression of being able to make extremely specific answers, but those answers are in fact statistically generic.

The psychic uses these statements to give the impression of being able to read minds and hear the secrets of the dead.

The chatbot gives the impression of an intelligence that is specifically engaging with you and your work, but that impression is nothing more than a statistical trick.

This idea was first planted in my head when I was going over some of the statements people have been making about the reasoning of these “AI.”

I first thought that these were just classic cases of tech bubble enthusiasm, but no, “AI” has both taken a different crowd and the believers in the “AI” bubble sound very different from those of prior bubbles.

—“This is real. It’s a bit worrying, but it’s real.”

—“There really is something there. Not sure what to think of it, but I’ve experienced it myself.”

—“You need to keep your mind open to the possibilities. Once you do, you’ll see that there’s something to it.”

That’s when I remembered, triggered by a blog post by Terence Eden on the prevalence of Forer statements in chatbot replies. I have heard this before.

This specific blend of awe, disbelief, and dread all sound like the words of a victim of a mentalist scam artist—psychics.

The psychic’s con is a tried and true method for scamming people that has been honed through the ages.

What I describe below is one variation. There are many variations, but the core mechanism remains the same.

The Psychic’s Con

The audience is represented by a collection of circles. The disinterested are in grey. The interested are in black

1. The Audience Selects Itself
Most people aren’t interested in psychics or the like, so the initial audience pool is already generally more open-minded and less critical than the population in general.

The circles now have different colours to indicate that they are not of a single demographic

2. The Scene is Set
The initial audience is prepared. Lights are dimmed. The psychic is hyped up. Staff research the audience on social media or through conversation. The audience's demographics are noted.

All the circles representing demographics not chosen are blurred

3. Narrowing Down the Demographic
The psychic gauges the information they have on the audience, gestures towards a row or cluster, and makes a statement that sounds specific but is in fact statistically likely for the demographic. Usually at least one person reacts. If not, the psychic will imply that the secret is too embarrassing for the "real" person to come forward, reminds people that they're available for private readings, and tries again.

A red box representing the psychic has an arrow pointing to the circle that represents the mark

4. The Mark is Tested
The reaction indicates that the mark believes they were “read”. This leads to a burst of questions that, again, sound very specific but are actually statistically generic. If the mark doesn’t respond, the psychic declares the initial read a success and tries again.

The mark's circle and the psychic's box have arrows pointing to each other representing a loop

5. The Subjective Validation Loop
The con begins in earnest. The psychic asks a series of questions that all sound very specific to the mark but are in reality just statistically probable guesses, based on their demographics and prior answers, phrased in a specific, highly confident way.

The mark's circle has an exclamation mark

6. “Wow! That psychic is the real thing!”
The psychic ends the conversation and the mark is left with the sense that the psychic has uncanny powers. But the psychic isn’t the real thing. It’s all a con.

1. Audience selection

Seers, tarot card readers, psychics, mind readers aren’t all con artists. Sometimes the “psychic” is open about it all just being entertainment and aren’t pretending to be able to contact spirits or read minds. Some psychics do not have a profit motive at all, and without the grift it doesn’t seem fair to call somebody a con artist.

But many of them are con artists deliberately fooling people, and they all operate using the same basic mechanisms that begin well before the reading proper.

The audience is usually only composed of those already pre-disposed to believe in psychic phenomena and those they have managed to drag with them. Hardcore sceptics will almost always be in a very small minority of the audience, which both makes them easy to manage and provides social pressure on them to tone down their scepticism.

Those who attend are primed to believe and are already familiar with the mythology surrounding psychics. All of which helps them manage expectations and frame their performance.

2. Setting the scene

Usually the audience is reminded of the ground rules for how psychic readings “work” at the start of the performance. They are helped by the popularisation of these rules by media, cinema, and TV.

Everybody now “knows” that:

Psychics also habitually research their audience, by mapping out their demographics, looking them up on social media, or even with informal interviews performed by staff mingling with attendees before the performance begins.

When the lights dim, the psychic should have a clear idea of which members of the audience will make for a good mark.

3. Narrowing down

The mark usually chooses themselves. The psychic makes a statement and points towards a row, quickly altering their gesture based on somebody responding visible to the statement. This makes it look like they pointed at the mark right from the beginning.

The mark is that way primed from the start to believe the psychic. They’re off-guard. Usually a bit surprised and totally unprepared for the quick burst of questions the psychic offers next. If those questions land and draw the mark in, they are followed by the actual reading. Otherwise, they move on and try again.

4. Testing the mark—Cold reading using subjective validation

The con—cold reading—hinges on a quirk of human psychology: if we personally relate to a statement, we will generally consider it to be accurate.

This unfortunate side effect of how our mind functions is called subjective validation.

Subjective validation, sometimes called personal validation effect, is a cognitive bias by which people will consider a statement or another piece of information to be correct if it has any personal meaning or significance to them. People whose opinion is affected by subjective validation will perceive two unrelated events (i.e., a coincidence) to be related because their personal beliefs demand that they be related.

As a consequence, many people will interpret even the most generic statement as being specifically about them if they can relate to what was said.

The more eager they are to find meaning in the statement, the stronger the effect.

The more they believe in the speaker’s ability to make accurate statements, the stronger the effect.

The basic mechanism of the psychic’s con is built on the mark being willing and able to relate what was said to themselves, even if it’s unintentional.

5. The subjective validation loop using validation statements

The psychic taps into this cognitive bias by making a series of statements that are tailored to be personally relatable—sound specific to you—while actually being statistically generic.

These statements come in many types. I use “validation statements” here as an umbrella term for all these various tactics.

Some common examples:

An important part of this process is the tone and bearing of the psychic. They need to be confident, be quick in dismissing errors and moving on when they make mistakes, and they need to be quick to read people’s expressions and body language and adjust their responses to match.

6. The con is completed

At the end of the process, the mark is likely to remember that the reading was eerily correct—that the psychic had an almost supernatural accuracy—which primes them to become even more receptive the next time they attend.

This is where the con often becomes insidious: the effect becomes stronger the more cooperative the mark is, and they often become more cooperative over time.

What’s more, susceptibility has nothing to do with intelligence.

Somebody raised to believe they have high IQ is more likely to fall for this than somebody raised to think less of their own intellectual capabilities. Subjective validation is a quirk of the human mind. We all fall for it. But if you think you’re unlikely to be fooled, you will be tempted instead to apply your intelligence to “figure out” how it happened. This means you can end up using considerable creativity and intelligence to help the psychic fool you by coming up with rationalisations for their “ability”. And because you think you can’t be fooled, you also bring your intelligence to bear to defend the psychic’s claim of their powers. Smart people (or, those who think of themselves as smart) can become the biggest, most lucrative marks.

Whereas the sceptic who thinks less of themselves is more likely to just go:

“That’s a neat trick. I don’t know how you pulled it off. Must be very clever.”

And just move on.

Many psychics fool themselves

It isn’t unusual for psychics to unconsciously develop a practice of cold reading subconsciously. The psychics themselves might not even be aware of their own tactics.

As Denis Dutton describes:

As a postgraduate student in pursuit of a scientific career, he became intrigued with astrology. Though during this period he had nagging doubts about the physical basis of astrology, he was encouraged to continue with it by his many satisfied clients, who invariably found his readings “amazingly accurate” in describing their personal situations and problems. Not until he had one day obtained such a gratifying reaction to a horoscope which, he realized later, he had cast completely incorrectly, did he begin slowly to understand the real nature of his activity: his great success as an astrologer had nothing whatsoever to do with the validity of astrology as a science. He had become, in fact, a proficient cold reader, one who sincerely believed in the power of astrology under the constant reinforcement of his clients. He was fooling them, of course, but only after falling for the illusion himself.

There are many examples of this easily found once you start doing the research. The mechanism is simple enough and already baked into people’s preconceptions of how readings work so many psychics accidentally develop the knack for it, meaning that they’re not just conning the person being read, they are also conning themselves.

This point will become important later.

The LLMentalist Effect

1. The Audience Selects Itself
People sceptical about "AI" chatbots are less likely to use them. Those who actively don't disbelieve the possibility of chatbot "intelligence" won't get pulled in by the bot. The most active audience will be early adopters, tech enthusiasts, and genuine believers in AGI who will all generally be less critical and more open-minded.

The circles now have different colours to indicate that they are not of a single demographic, all overlaid by the word 'Hype' and arrows indicating a prevailing atmosphere of hype.

2. The Scene is Set
Users are primed by the hype surrounding the technology. The chat environment sets the mood and expectations. Warnings about it being “early days” and “hallucinations” both anthropomorphise the bot and provide ready-made excuses for when one of its constant failures are noticed.

All the circles representing demographics not chosen are blurred

3. The Prompt Establishes the Context
Each user gives the chatbot a prompt and it answers. Many will either accept the answer as given or repeat variations on the initial prompt to get the desired result. They move on without falling for the effect. But some users engage in conversation and get drawn in.

Various circles representing marks are connected via loop arrows with boxes representing the chatbot. The rest are blurred

4. The Marks Test Themselves
The chatbot’s answers sound extremely specific to the current context but are in fact statistically generic. The mathematical model behind the chatbot delivers a statistically plausible response to the question. The marks that find this convincing get pulled in.

The mark's circle and the chatbot's box have arrows pointing to each other representing a loop.

5. The Subjective Validation Loop
The mark asks a series of questions and all of the replies sound like reasoned answers specific to the context but are in reality just statistically probable guesses. The more the mark engages, the more convinced they are of the chatbot’s intelligence.

The mark's circle has an exclamation mark

6. “Wow! This chatbot thinks! It has sparks of general intelligence!”
The mark is left with the sense that the chatbot is uncannily close to being self-aware and that it is definitely capable of reasoning But it’s nothing more than a statistical and psychological effect.

1. The audience selects itself

If you aren’t interested in “AI”, you aren’t going to use an “AI” chatbot, and if you try one, you’re less likely to return.

This means that many of the avid users of these chatbots are self-selected to be enthusiastic and open-minded about the field of AI and the notion of Artificial General Intelligence (AGI)—that these technologies might lead to self-aware and self-improving reasoning systems.

Those who are genuine enthusiasts about AGI—that this field is about to invent a new kind of mind—are likely to be substantially more enthusiastic about using these chatbots than the rest.

This parallels the audience selection for the psychic’s con. Those who believe in an afterlife and that it can be contacted by the living are substantially more likely to attend a psychic’s reading than others.

2. Setting the stage

Our current environment of relentless hype sets the stage and builds up an expectation for at least glimmers of genuine intelligence. For all the warnings vendors make about these systems not being general intelligences, those statements are always followed by either an implied or an actual “yet”. The hype strongly implies that these are “almost” intelligences and that you should be able to perceive “sparks” of intelligence in them.

Those who believe are primed for subjective validation.

The warnings also play a role in setting the stage. “It’s early days” means that when the statistically generic nature of the response is spotted, it’s easily dismissed as an “error”. Anthropomorphising concepts such as using “hallucination” as a term help dismiss the fact that statistical responses are completely disconnected from meaning and facts. The hype and mythology of AI primes the audience to think of these systems as persons to be understood and engaged with, all but guaranteeing subjective validation.

3. The prompt establishes the context

The initial prompt interaction is the first filter. Most will just take the first answer and leave, or at most will repeat variations of their prompt until they get the result they wanted. These interactions are purely mechanical. The end-user is treating the chatbot merely as a generative widget, so they never get pulled into the LLMentalist effect.

Some of the end-users, usually those who are more enthusiastic about the prospect of “AI”, begin to engage and get pulled into “conversation” with a mathematical language model.

4. The mark tests themselves—subjective validation kicks in

That conversation is the primary filter. Those who want to believe will see the responses to their prompt as being both specifically about them and intelligent. They are primed to see the chatbot as a person that is reading their texts and thoughtfully responding to them. But that isn’t how language models work. LLMs model the distribution of words and phrases in a language as tokens. Their responses are nothing more than a statistically likely continuation of the prompt.

You give it text. It gives you a response that matches responses that texts like yours commonly get in its training data set.

Already, this is working along the same fundamental principle as the psychic’s con: the LLM isn’t “reading” your text any more than the psychic is reading your mind. They are giving you statistically plausible responses based on what you say. You’re the one finding ways to validate those responses as being specific to you as the subject of the conversation.

Because of how large the training data set is, the responses from the chatbot will look extremely convincing and specific, even though they are statistically generic. Once you’ve trained on most of the past twenty years of the web, large collections of stolen ebooks, all of Reddit, most of social media, and a substantial amount of custom interactions by low-wage workers, the model will have a response for almost everything you can think of, or can use a variation of something it’s already seen.

These initial interactions can be quite compelling, especially if you’re a believer in “AI”, but it is in the longer and repeated conversations that the effect really begins to kick in.

5. The subjective validation loop—RLHF enters the picture

It’s important to remember at this stage how Reinforcement Learning through Human Feedback works.

This is the method that vendors use to turn a raw language model into a chatbot that can hold a conversation.

RLHF doesn’t let the vendor make specific corrections to an LLM’s output. The method involves using human feedback to rank a variety of texts generated by the model, usually following some other form of fine-tuning. The ranked texts are in turn used to train a separate reward model. It’s this model that is responsible for the actual Reinforcement Learning of the LLM. The reward model, coupled with fine-tuning the LLM on collections of chats, is what turns the borderline unhinged conversations of a regular model into the fluent experience you see in systems such as ChatGPT.

Because the feedback is based on rankings, it can’t easily be based on specific issues. If a model makes a false statement in a conversation, that conversation gets a lower rank.

This lack of concrete specificity likely means that RLHF models in general are likely to reward responses that sound accurate. As the reward model is likely just another language model, it can’t reward based on facts or anything specific, so it can only reward output that has a tone, style, and structure that’s commonly associated with statements that have been rated as accurate.

Even the ratings themselves are suspect. Most, if not all, of the workers who provide this feedback to AI vendors are low-paid workers who are unlikely to have specialised knowledge relevant to the topic they’re rating, and even if they do, they are unlikely to have the time to fact-check everything.

That means they are going to be ranking the conversations almost entirely based on tone and sentence structure.

This is why I think that RLHF has effectively become a reward system that specifically optimises language models for generating validation statements: Forer statements, shotgunning, vanishing negatives, and statistical guesses.

In trying to make the LLM sound more human, more confident, and more engaging, but without being able to edit specific details in its output, AI researchers seem to have created a mechanical mentalist.

Instead of pretending to read minds through statistically plausible validation statements, it pretends to read and understand your text through statistically plausible validation statements.

The validation loop can continue for a while, with the mark constantly doing the work of convincing themselves of the language model’s intelligence. Done long enough, it becomes a form of reinforcement learning for the mark.

6. The marks become cheerleaders

The most enthusiastic believers in an imminent AI revolution are starting to sound very similar to long-time believers in psychics and mind-reading.

They come up with increasingly convoluted ideas and models to explain why the impossible is possible. They become more and more dismissive of fields of science and research that challenge their world view. Their own statements become tinged with awe and dread.

And they keep evangelising. This is real!

Often followed by: This is dangerous!

Remember, the effect becomes more powerful when the mark is both intelligent and wants to believe. Subjective validation is based on how our minds work, in general, and is unaffected by your reported IQ.

If anything, your intelligence will just improve your ability to rationalise your subjective validation and make the effect stronger. When it’s coupled with a genuine desire to believe in the con—that we are on the verge of discovering Artificial General Intelligence—the effect should both be irresistible and powerful once it takes hold.

This is why you can’t rely on user reports to discover these issues. People who believe in psychics will generally have only positive things to say about a psychic, even as they’re being bilked. People who believe we’re on the verge of building an AGI will only have positive things to say about chatbots that support that belief.

It’s easy to fall for this

Falling for this statistical illusion is easy. It has nothing to do with your intelligence or even your gullibility. It’s your brain working against you. Most of the time conversations are collaborative and personal, so your mind is optimised for finding meaning in what is said under those circumstances. If you also want to believe, whether it’s in psychics or in AGI, your mind will helpfully find reasons to believe in the conversation you’re having.

Once you’re so deep into it that you’ve done a press tour and committed yourself as a public figure to this idea, dislodging the belief that we now have a proto-AGI becomes impossible. Much like a scientist publicly stating that they believe in a particular psychic, their self-image becomes intertwined with their belief in that psychic. Any dismissal of the phenomenon will feel to them like a personal attack.

The psychic’s con is a mechanism that has been extraordinarily successful at fooling people over the years. It works.

The best defence is to respond the same way as you would to a convincing psychic’s reading: “That’s a neat trick, I wonder how they pulled it off?”

Well, now you know.

Once you’re aware of the fallibility of how your mind works, you should have an easier time spotting when that fallibility is being exploited, intentionally or not.

That brings us to an important question.

Is this intentional?

Given that there are billions of dollars at stake in the tech industry, it would be tempting to assume that the statistical illusion of intelligence was intentionally created by people in the tech industry.

I personally think that’s extraordinarily unlikely.

A popular response to various government conspiracy theories is that government institutions just aren’t that good at keeping secrets.

Well, the tech industry just isn’t that good at software. This illusion is, honestly, too clever to have been created intentionally by those making it.

The field of AI research has a reputation for disregarding the value of other fields, so I’m certain that this reimplementation of a psychic’s con is entirely accidental. It’s likely that, being unaware of much of the research in psychology on cognitive biases or how a psychic’s con works, they stumbled into a mechanism and made chatbots that fooled many of the chatbot makers themselves.

Remember what I wrote above about psychics frequently having conned themselves, that many of them aren’t even aware of their own scam?

The same applies here. I think this is an industry that didn’t understand what it was doing and, now, doesn’t understand what it did.

That’s why so many people in tech are completely and utterly convinced that they have created the first spark of true Artificial General Intelligence.

This new era of tech seems to be built on superstition and pseudoscience

Once I started to research the possibility that LLM interactions were a variation on the psychic’s con, I began to see parallels everywhere in the field of “AI”.

All of these are proposed applications of “AI” systems, but they are also all common psychic scams. Mind reading, police assistance, faith healing, prophecy, and even psychic employee vetting are all right out of the mentalist playbook.

Even though I have no doubts that these efforts are sincere, it’s becoming more and more obvious that the tech industry has given itself wholesale to superstition and pseudoscience. They keep ignoring the warnings coming from other fields and the concerns from critics in their own camp.

Large Language Models don’t have the functionality or features to make up for this wave of superstition.

Taken together, these flaws make LLMs look less like an information technology and more like a modern mechanisation of the psychic hotline.

Delegating your decision-making, ranking, assessment, strategising, analysis, or any other form of reasoning to a chatbot becomes the functional equivalent to phoning a psychic for advice.

Imagine Google or a major tech company trying to fix their search engine by adding a psychic hotline to their front page? That’s what they’re doing with Bard.

—“Our university students can’t make heads nor tails of our website. Let’s add a psychic hotline!”

—“We need to improve our customer service portal. Let’s add a psychic hotline!”

—“We’ve added a psychic hotline button to your web browser! No, you can’t get rid of it. You’re welcome!”

—“Can’t understand a thing in our technical docs? Refer to our fancy new psychic hotline!”

The AI bubble is going to be a tough one to weather.

More on “AI”

I’ve spent some time writing about the many flaws of language models and generative “AI”.

I’ve come to the conclusion that a language model is almost always the wrong tool for the job.

I strongly advise against integrating an LLM or chatbot into your product, website, or organisational processes.

If you do have to use generative AI, either because it’s a mandate from above your pay grade or some other requirement, I have written a book that’s specifically about the issues with using generative “AI” for work:

The Intelligence Illusion: a practical guide to the business risks of Generative AI.

It’s only $35 USD for EPUB and PDF, which is only 15% of the $240 USD cost of twelve months of ChatGPT Plus.

But, again, I’d much rather you just avoid using a language model in the first place and save both the cost of the ebook and the ChatGPT subscription.

References on the Psychic’s Con

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