GPT

Thesis # ELIZA in 1970 was a toy – a mirror in a cardboard frame. ChatGPT in 2025 is a distorted mirror with a golden edge. Not more intelligent – just bigger, better trained, better disguised.

There are two dominant narratives about Large Language Models: Narrative 1: “AI is magic and will replace us all!” → Exaggerated, creates hype and fear Narrative 2: “AI is dumb and useless!” → Ignorant, misses real value

“It’s like comparing apples and pears — but what if you don’t know what either is? Welcome to GPT.” The debate around artificial intelligence often ignores a critical fact: Large Language Models like GPT do not understand semantic concepts. They simulate understanding — but they don’t “know” what an apple or a pear is. This isn’t just academic; it has real-world implications, especially as we increasingly rely on such systems in decision-making.

What actually happens to your prompt before an AI system responds? The answer: a lot. And much of it remains intentionally opaque. This post presents scientifically documented control mechanisms by which transformer-based models like GPT are steered – layer by layer, from input to output. All techniques are documented, reproducible, and actively used in production systems.

ELIZA was a parrot with rules – GPT is a chameleon with probabilities. Yet both are symbolic manipulators without true understanding.

Translations: DE GPT and similar models simulate comprehension. They imitate conversations, emotions, reasoning. But in reality, they are statistical probability models, trained on massive text corpora – without awareness, world knowledge, or intent.