What’s Wrong With AI Content: Why AI Lie About Gambling

Why you shouldn’t trust AI when it talks about gambling or anything niche

ChatGPT lies so convincingly that catching it is incredibly difficult.

I was reminded of this after reading a recent post on the Casinomeister forum. The author misinterpreted how PRNGs (Pseudorandom Number Generators) work in slot machines and came to the wrong conclusions about randomness and odds. Helping him down that path? Copilot…

This example perfectly illustrates a problem most people don’t even consider.

How language models actually work

In simple terms, large language models (LLMs) predict the next word in a sequence. They’re extremely good at this thanks to massive training datasets. But they don’t “know” anything. They don’t think. They don’t understand. They just generate statistically likely sequences of words based on patterns in the data — text that often sounds meaningful, even when it isn’t.

When the training data is rich and accurate, the output can feel coherent and even insightful. But that illusion of understanding breaks down quickly when the data is poor or incomplete.

LLMs aren’t built to say “I don’t know.” They’re designed to keep going. So when information is missing or unreliable, they fill the gaps — often with loose associations, half-truths, or entirely fabricated but plausible-sounding claims.

The problem with source quality

Take gambling, for example — an area I happen to know quite a bit about. High-quality content in this space is rare. Even in books, you’ll find nonsense like “roulette secrets” and guides on predicting where the ball will land. Online articles are often worse — rewrites of rewrites, riddled with errors.

Add to that a sea of influencers with huge followings spreading bad information — often in good faith — and you get a feedback loop. For example, Trainwreckstv regularly talks about max wins to millions of viewers — confidently, but incorrectly. The problem is, those claims get picked up and repeated as fact.

AI trains on all of this. Then it spits it back out in a format that sounds trustworthy.

Three common AI content failures

1. Mixing truth with error

AI is great at surface-level info. It can explain what RTP is, list popular providers, or describe basic mechanics. But if you ask for a list of onshore casino licenses, it might throw in Malta alongside the UK, New Jersey, Michigan, and Spain. Malta is offshore. Whether you trust it or not is beside the point — it distorts the picture.

2. Hallucinations

When AI doesn’t know something, it invents. If you ask for sources or proof, there often aren’t any. So now you’re not just dealing with misinformation from weak sources, but entirely fabricated “facts” that sound real.

3. Repeating common myths

Even so-called expert books sometimes promote debunked ideas like “roulette prediction.” Articles online echo the same myths.

AI is trained on all of this and regurgitates it with confidence.

Why people trust it anyway

AI presents nonsense with so much authority that even intelligent readers get fooled. Especially outside their own area of expertise.

Here’s why:

• We instinctively trust “smart machines”

• The tone is confident and declarative, never uncertain

• AI never says “I’m not sure”

• The language mimics real experts, using convincing jargon and structure

How to use AI responsibly

I’m not anti-AI. It’s a powerful tool. But you have to treat it with caution.

Where it works well:

• Basic research on broad topics

• Translating texts

• Organizing and formatting information

• Explaining common concepts

Where to be careful:

• Specialized or technical subjects

• Statistics and hard numbers

• Legal and medical advice

• Anything that could impact real-world decisions

Final thought

LLMs are great assistants, but unreliable authors in niche domains. They’re not the ultimate source of truth. Still, in some areas they already outperform the average human. In medicine, for example, there are studies showing this.

Is that because the average specialist isn’t very good? Or because the training data in that field is just much better?

Let me know what you think in the comments. Rate how well AI performs in the domain you know best. Because without solid background knowledge, it’s dangerously easy to walk away with the wrong impression.

And despite everything I just said, this post probably gave you better answers than a search engine would.

By the way, was this written by a human or AI? Take a guess 😏