So, you’ve just come back from a meeting and everyone in it was chattering about AI. You’re hearing on the news about AI spinning up human DNA to help solve cancer, but then you’ve also asked your AI assistant to write you a work email response and it sounds bland, boring and overly salesy.
ChatGPT, Claude, Gemini, CoPilot, Perplexity, Sora 2, Deepseek, Qwen, and Mistral. What’s the difference between them all?
And why have all my software providers come up with their own versions by bolting AI onto their existing products. Think Salesforce AI, Hubspot AI, Zendesk AI, Klaviyo AI, MailChimp AI. Why do I need so many options? Can’t I just use one to do everything? Wouldn’t that be more artificially intelligent?
Here’s the problem: most people think of AI as a single thing. A category. A product, even. For some, it’s that chatbot their team started using. For others, it’s a vague, all-encompassing force intent on reshaping the world.
The truth is that “AI” today is not one thing. It’s an entire industry, with dozens of companies, hundreds of models, and thousands of products built on top of them. Each with different strengths, different costs, different designs, and different jobs they’re built to do. Lumping them together is a bit like applying the term “vehicle” to a Formula One car, a delivery van, and a forklift. Technically it’s accurate, but practically it’s useless.
So here’s a way to make sense of it all: stop thinking of AI as software, and start thinking of it as a vehicle. Engines, fuel, chassis, the whole lot. Because once you look at the AI landscape that way, almost everything that felt confusing starts to click: why some tools cost a fortune to run and others cost pennies, why a model that’s brilliant at one task falls flat at another, and why your Microsoft rep swears by Copilot while your developers are quietly using something else entirely. Just as there’s simply no one car that will suit every human, no matter what Tesla owners say, there’s no one AI to rule them all. Well, not yet anyway.
This is the first in a three-part series of AI for business leaders. In this piece, we’ll pop the bonnet on the engines that power AI and the vehicles that put them in your hands. In Part 2, we’ll look at the business realities: fuel costs, data, security, and how the smartest organisations run AI as a logistics network. In Part 3, we’ll get to the humans behind the wheel, what happens when the cars start driving themselves, and the rules of the road that keep the whole thing from becoming a demolition derby.
Let’s pop the bonnet.
At the heart of every AI tool sits an engine. This is what the industry calls a foundation model. The engine is the bit that actually thinks: it understands language, generates ideas, analyses data, writes code and even draws pictures.
Building one of these engines is brutally hard, brutally expensive, and brutally well funded. Which is why there are only a handful of genuine engine makers in the world. Three of them dominate the conversation (note: these car references are relative to the Australian car market):
Then there are the open-source companies, who take a different approach entirely, giving their engines away free for anyone to download, modify and build on. Think of it as a car manufacturer publishing its engine blueprints so that home builders, workshops, and rival car makers can all tinker with them. This open-source approach has changed the industry, because it means you don’t have to rent an engine from OpenAI or Anthropic if you’d rather build your own.
Chinese companies have come to lead much of this space. Players like DeepSeek, Qwen, Kimi and others are doing to AI what BYD, Chery, and MG are doing to the Australian car market: arriving fast, undercutting on price, and quietly, and quickly, matching the performance of the established names. DeepSeek sent shockwaves through the industry by releasing models that rivalled the frontier engines at a fraction of the cost.
Rounding out the field are the boutique tuning houses. Mistral, the French outfit, builds elegant, efficient engines with a European sensibility, a bit like Peugeot or Citroen, while xAI, Elon Musk’s contribution to the space, makes Grok, which you can think of as the Tesla of AI: polarising, attention-grabbing, and unmistakably the work of its founder.
Here’s the catch most business buyers miss: with the big three, you don’t buy the engine. You rent access to it. They build it, they shape it, they decide how it behaves, and you drive it only in the ways that they allow. It’s a bit like leasing a Mercedes: you get a great car, but you can’t pop the bonnet and start modifying things.
Open-source engines like Llama and DeepSeek flip that on its head: free to take home, free to crack open, free to tune for your own industry. That distinction matters a lot if you care about data sovereignty (where your information lives and who can see it) or if you want a model that knows your specialism inside out.
Just as some car companies make both V12s and economical four-cylinders, AI labs build a whole catalogue of engines. The trick is knowing which is which:
The lesson is simple: using the most powerful engine for every task is like driving a Ferrari to pick up the kids from school. It works, but it’s wasteful, expensive, and not as efficient as the right tool for the job.
An engine on its own is unusable. Nobody drives an engine. You drive a car built around one. In AI, that car is the application: the interface, the controls, the safety features, the experience that turns raw intelligence into something a human can actually use.
This is the layer where most of the brand confusion lives, because the same engine often powers very different vehicles, designed with very different drivers in mind. They tend to fall into three groups.
These are the AI products you open up and use as their own thing. You go to them, log in, and start a conversation.
These are AI products engineered for a specific job or industry. They might use the same engines as the standalone vehicles, but they’re wrapped in a chassis built for a particular kind of work.
These aren’t new vehicles at all. They’re AI features added to software you already drive. The existing tool gets smarter without you having to buy something new.
You’ll find the same engines under the bonnet in many cases, but wildly different vehicles around them, because each is built for a different journey.
If this is starting to make sense, you’ve already got a sharper mental model of the AI landscape than most people in the meetings you’re sitting in. You can spot the difference between an engine maker and a vehicle. You can tell when two products are running the same engine under different brands. You can match the engine to the job rather than driving a Ferrari to school pickup.
But understanding the vehicles is only the first step. The real questions for any business start once you decide to actually buy and run a fleet. What does it cost to keep these things on the road? Whose data are they carrying, and who else can see it? How do they connect to each other across your business?
That’s where Part 2 picks up - 'The Business Realities of Running an AI Fleet'. We’ll look at the fuel that powers AI, the cargo it carries, and how the smartest organisations are running their AI not as a collection of tools, but as a logistics network.
Read on.
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