What's inside ChatGPT? Large Language Models, explained.

When executives ask what ChatGPT is, I start with what it isn't. It's not a thinking machine. It doesn't understand anything in human terms. No opinions. No experiences. No world knowledge. It doesn't even have memory in the traditional sense. Every conversation starts from scratch, even if some providers now paper over that with technical workarounds.

What it does have is a statistical model trained on billions of texts. It spots patterns in language and predicts the most likely next word. Sounds simple. It's so powerful, though, that the output reads as if a human wrote it. It answers questions, summarises documents, translates languages and writes code that runs.

That basic grasp matters for any executive making AI investment decisions. Know how an LLM works and you can play to its strengths and sidestep its weaknesses. Treat it as an all-knowing AI and you'll be disappointed. Worse, you'll make poor calls about where to deploy it in your business. The technology is impressive. But it's a tool, not an oracle.

GPT-4 from OpenAI. Claude from Anthropic. Gemini from Google. Llama from Meta. The LLM market is expanding fast. For businesses, the question is simple: which model fits which use case? And is it worth betting on one, or hedging across several?

The differences come down to training data, architecture and safety rails. GPT-4 is the best-known all-rounder, with the widest ecosystem. Claude handles long texts, careful analysis and sensitive topics with more caution. Gemini integrates neatly with Google services and is strong on multimodal tasks. Llama is open source, so you can run it on your own servers. That matters for businesses with strict data protection rules that rule out sharing data with third parties.

In practice, though, the leading models deliver broadly comparable results across most enterprise use cases. Model choice matters less than whether your people can get the best out of them. A decent model with a sharp prompt beats the best model with a weak one. Which is why I tell companies: invest in your people's AI skills, not just the licences.

LLMs "hallucinate". They produce statements that sound plausible and turn out to be wrong. They invent sources that don't exist. They muddle facts and deliver nonsense with the same confidence as accurate information. This isn't a bug that'll be patched in the next release. It's baked into how the technology works. Know that, and you'll check the output. Don't know it, and you'll push errors out into your business.

Then there's bias. Models reflect the prejudices in their training data. You see it most clearly in content about careers, culture and gender. An LLM trained on the internet has learned the internet's biases. If you're using AI in decisions that affect people - recruitment, customer service, comms - you need to factor that in and build in safeguards.

And knowledge limits. Every model has a training data cut-off. It doesn't know what happened after it. Current events, new legislation, fresh market data: for any of that you need Retrieval-Augmented Generation (RAG) or similar, feeding the model up-to-date information. Not a dealbreaker, but something to design into your AI architecture from the start.

You don't need to follow the maths behind transformer architectures. You don't need to grasp attention mechanisms versus feed-forward layers. You do need to understand what an LLM can do, what it can't, and when it makes sense for your business. That ability to judge AI sensibly is the foundation of any strategy beyond "let's have a play with ChatGPT".

I see it in my AI fundamentals talks again and again. The real value isn't in the technical detail. It's in taking the mystery out of it. When a CEO tells me after 60 minutes "Now I see why ChatGPT sometimes talks nonsense, and how we work with it productively anyway" - job done. Uncertainty turns into confidence. Blind faith or blanket scepticism turns into an informed position.

Because you can only make sound calls on AI investment, data protection and use cases when you can actually assess the technology. Not as a computer scientist. As a decision-maker. That's what I deliver in these talks: clear, focused insight that connects directly to your day-to-day business. The question isn't whether your company will use AI. It's how deliberately, and how well.