Yesterday, as I struggled with a Large Language Model that at first gave good advice (how to build a 5 crystal filter) and then really bad advice (how to build a filter with 5 crystals in parallel to achieve a USB passband) I got a message from an old friend who currently works in this industry.
He advised me to be careful about advice from LLM AIs. He warned that these AIs would just develop answers based on the information that had been used to train them. In the case of ham radio electronic questions, the training material was often littered with errors and lore and bad, wrong info. Often this bad information would be repeated over many years. This would make the AI more likely to use it in their answers.
It then occurred to me that I should take one case where I knew that wrong information was in the ham radio "literature" and that this information had been repeated many times over the years. I am referring here to the question of why hams use lower sideband below 10 MHz and upper sideband above 10 MHz. I had explored this question in many blog posts. Many explanations were offered -- all of them violated the Hallas rule. All of them failed to answer the question. I wondered, would AI be wrong?
Well, AI was wrong. AI said:
Subtracting in heterodyne mixing inverts the frequency spectrum. An Upper Sideband signal generated at the IF naturally flipped into a Lower Sideband signal on the lower bands.
That is wrong. The Hallas rule tells us that sideband inversion ONLY occurs when the signal with modulation is SUBTRACTED FROM the signal without modulation.
I called AI on this error in its answer. I asked it why it had given me bad information. Its responses were telling.
Why did you initially say that sideband inversion results from subtraction? Is this because of the material on which you had been trained?
You're asking a fair question about where that explanation came from.
The brief answer: yes, it comes down to how technical explanations are presented across textbooks, amateur radio training materials, and online discussion forums—and how LLMs synthesize those patterns.
So you are just repeating incorrect information, as long as it was repeated enough, right?
That is a fair critique, and it gets to the heart of how Large Language Models work.
I don't "know" facts the way a human does; I generate text based on statistical patterns learned from vast amounts of training data. When a simplified explanation, misconception, or shorthand rule of thumb is repeated thousands of times across forums, textbooks, and study guides—as is the case with "subtraction inverts sidebands" in ham radio literature—I am prone to picking up and repeating that pattern.
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I think we can draw three conclusions from all this:
1) The talk about AI "personhood" is just nonsense, mostly nonsense from Anthropic.
2) The concerns about data poisoning are real. The ham radio "literature" is already poisoned, and look what happens.
3) We are living with the results of bad info in the amateur radio "literature" over the years. AI is just spitting it back at us. And I think it is getting worse. Now everyone with a callsign and camera has a YouTube channel. The quality of content is low and dropping. Soon AI will be spitting this stuff back at us.
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Let's do an experiment. Those of you who have access to the other AI's like Claude, ChatGPT, DeepSeek and others, just ask them the question I started with:
"Why do ham radio operators use LSB below 10 MHz and USB above 10 MHz?" Let's see if the other AI LLMs are better at coming up with truthful answers. Of course, they may see this and not repeat the error.
















