Not All AI Is Generative: Understanding the Difference

It’s 2025, and everyone is talking about AI. That’s no surprise when you consider that there are now around 70,000 AI companies worldwide, a number that’s growing every day as innovators discover new ways to apply AI to research, business, and everyday life.

But not all artificial intelligence is created the same. At the moment, there are two very distinct types of AI: traditional and generative. And, before we get into how they differ, we need to understand where AI started, and how we got here.

A Quick History of AI

Contrary to what many people believe, AI isn’t something new—in fact, it’s been around for over 70 years.

In 1950, mathematician and computer scientist Alan Turing published his paper “Computer Machinery and Intelligence” and developed The Imitation Game (also known as “The Turing Test”) to determine whether or not machines can think like humans.

Just two years after Turing’s breakthrough publication, computer scientist Arthur Samuel developed an early AI program that learned to play checkers using a defined set of rules and the process of trial and error.

By 1955 machine learning research was in full swing, and John McCarthy, a mathematician and early computer science pioneer, coined the term “artificial intelligence,” which we still use today.

These developments, among others, marked the birth of traditional artificial intelligence. But what, exactly, is traditional AI, and how does it differ from generative AI?

What is Traditional AI?

Traditional AI, also known as narrow or rule-based AI, works by using a set of pre-programmed rules—also known as algorithms—to make deterministic decisions. This means that given the same input, traditional AI will always produce the same output, following explicit logical rules defined by human programmers—a process that requires substantial expertise. Once these rules are defined, this type of AI will not operate outside the strict boundaries of its design. But, within those boundaries, it can be an incredibly powerful and reliable tool.

Let’s use Arthur Samuel’s checkers program as an example—his early AI worked because it was provided the strict rules of the game and allowed to run through trial and error scenarios. As good as it became at checkers, it could never play any other games, unless Samuel also explicitly programmed those rules as well.

But just because traditional AI requires explicit programming, it doesn’t mean that it’s not extremely useful. Traditional AI is the perfect tool for solving many of our day-to-day problems, solving domain-specific problems and ensuring that consumer and industry technologies are both reliable and predictable. In fact, if you’re reading this blog post, odds are that you already use traditional AI hundreds of times per day when interacting with the digital world.

Let’s take a look at some examples of what that might look like:

• Streaming services that suggest movie recommendations based on what you’ve liked in the past
• Email platforms that automatically filter out spam mail
• Search result rankings on your browser
• Fraud detection services from your bank
• Medical diagnostic systems that help doctors identify diseases based on symptoms and test results
• GPS navigation systems that calculate optimal routes based on traffic patterns
   

What is Generative AI?

When it comes to artificial intelligence, generative AI is the new kid on the block. Unlike traditional AI, which follows deterministic rules, generative AI operates probabilistically, meaning it generates outputs based on the statistical patterns it learned, with some degree of randomness built in. 

Rather than following pre-programmed rules, generative AI systems require massive human effort in a different way: teams of researchers and engineers must carefully curate training datasets, design neural network architectures, and fine-tune models through extensive human feedback. The learning process involves analyzing enormous datasets to build neural networks—machine learning models that, when trained, can generate entirely new content by predicting what should come next based on statistical patterns.

Generative AI is a fairly recent development, exploding in popularity with the emergence of large language models (LLMs) and tools like ChatGPT and Claude. Nowadays, AI companies are able to build a variety of different learning models to help organizations and people further their goals efficiently and effectively. The probabilistic nature of generative AI means that the same input can produce different outputs each time, allowing for creativity and variety, but also introducing some unpredictability. 

Here are a few examples of generative AI in use today:

• Text generation apps like ChatGPT, Claude, and Bard
• Image generators like DALL-E and Midjourney
• AI coding tools like Cursor and GitHub Copilot
• Voice synthesis tools like ElevenLabs
   

While all these tools are extremely helpful, generative AI does have its downsides. Some companies have recently come under scrutiny for training learning models on other creators’ work, and many others are worried about becoming too reliant on AI and losing our ability to think critically and creatively.

We still have a long way to go in ensuring that generative AI is developed and implemented in an ethical, smart, and safe way. That said, it remains a promising field that has the potential to make a huge positive impact.

Resource Consumption: A Key Difference

These two types of AI systems also differ in another crucial way—their effect on the environment. Traditional AI systems don’t rely on complex neural network computations, so they typically use significantly less energy and resources.

Generative AI, on the other hand, requires enormous computational power for both training and operation. Training a single large language model often uses the same amount of electricity that hundreds of homes use in a year, and the process requires huge amounts of water resources for cooling data centers. Even running generative AI applications requires more energy per query than traditional AI tools.

In certain cases, generative AI can be used to help the environment. That said, it’s still important to consider resource demands when deciding which tool to use.

Around the world, researchers are working to create new artificial intelligence tools, both traditional and generative, that can help us simplify our lives, make new discoveries, and progress as a society.

Choosing between the two depends on your specific needs: traditional AI is a great choice for reliable, predictable, and domain-specific tasks that need explainable results and lower resource consumption. Generative AI, on the other hand, is great for creative, flexible applications where variety and the ability to handle new situations are more important than perfect consistency.

As users of artificial intelligence, it’s our job to keep ourselves informed and educated about what these tools are and how we can best use them in our lives.

It’s an exciting time for AI, and we can’t wait to see what’s next.

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AI is redefining the future of the tech industry. Want to stay prepared? Check out these 4 AI skills you’ll need for tomorrow’s tech jobs.