Many feel they're missing foundational knowledge about how these systems actually work. They can follow tutorials on prompting and use the tools, but there's this nagging uncertainty – like driving a car without understanding what's happening under the hood.

I tell a lot of them that honestly…it doesn’t matter that much! Just learn to use the tools.

BUT understandably people still worry. They lack confidence because of this gap in their knowledge. What if a client asks about how ChatGPT works? What is a customer asks a technical question?

This knowledge gap isn't just academic – it has real business consequences. I've seen consultants struggle to explain AI capabilities to clients, entrepreneurs waste time trying approaches that fundamentally won't work with current AI, and advisors making strategic recommendations based on misconceptions about what AI can and can't do.

This week, we're going to fix that. We'll build a practical foundation of AI knowledge that helps you make better business decisions, communicate more effectively about AI, and cut through the hype to see the real opportunities.

From "If-Then" to "I Think..."

Last week I was chatting with a developer friend who's been coding since the early 90s. He said something that stuck with me: "In the old days, I had to tell the computer exactly what to do, step by step. Now I just ask it nicely, and it figures things out on its own."

The most profound change is in how we interact with computers. We've moved from:

Programming paradigm: "I will tell you exactly what to do, step by step."

AI paradigm: "I'll tell you what I want to achieve, and you figure out how to do it."

We've gone from painstakingly programming explicit instructions to having conversations with machines that seem to "get it." This fundamental shift isn't just interesting tech trivia – it completely changes how entrepreneurs like us can leverage technology to build and grow our businesses.

Traditional programming is like creating a detailed recipe: "If this happens, then do that." Computers follow these explicit instructions perfectly but have zero flexibility. If you didn't anticipate something in your code, the computer would be completely stuck.

Machine learning introduced a new approach: instead of writing explicit rules, we started showing computers many examples and letting them detect patterns. This was a bit like training a dog – it couldn't explain why it made certain decisions, but it could recognise patterns with remarkable accuracy after seeing enough examples.

What we have now with generative AI and large language models is something entirely different. These systems have ingested vast amounts of human knowledge and can now generate new content, reason through problems, and engage in meaningful dialogue.

It's like the difference between:

• A calculator (traditional programming)

• A trained animal that can categorise things (machine learning)

• A knowledgeable assistant that can understand, create, and explain (generative AI)

A Brief History: Key Milestones

AI's evolution hasn't been a straight line – it's been a series of breakthroughs, setbacks, and paradigm shifts spanning over 70 years. It’s not new.

Basically we’ve been trying to build AI since computers were imagined: Alan Turing envisioned how computers could be build and at the same time speculated that they might one day simulate human intelligence. Pretty smart guy!

Here are the pivotal moments:

1950s: The Birth of AI Alan Turing proposed his famous test for machine intelligence, and the field of AI was officially named at the Dartmouth Workshop in 1956. Early researchers were wildly optimistic, believing true machine intelligence was just around the corner.

1970s-1980s: The First AI Winter Early enthusiasm gave way to disappointment as researchers hit hard limitations. Funding dried up as promised breakthroughs failed to materialise.

1990s-2000s: The Rise of Machine Learning Rather than trying to program intelligence directly, researchers focused on statistical approaches where systems could learn from data. This change in direction slowly revitalized the field.

2012: The Deep Learning Revolution A neural network dramatically outperformed traditional methods in the ImageNet competition, triggering massive investment in deep learning approaches.

2022-Present: The Generative AI Era The public release of ChatGPT and similar systems brought sophisticated AI capabilities to the general public, democratizing access to these powerful tools virtually overnight.

For those interested in a deeper dive into AI history, I highly recommend:

• This excellent video overview: