AI: We're naming it wrong, and why it matters

Prefer watching a video? I transformed this article into a video presentation using Google’s NotebookLM. It’s surprisingly accurate and even expands on some of the ideas. Give it a watch!

In the world of technology, we have a chronic problem: we’re often terrible at naming things!

And I’m not just talking about uninspired marketing names — honestly, OpenAI, you created a revolution in human civilization and the best name you could come up with was “ChatGPT”?! 😄

I mean fundamental concepts, starting with the very term “Artificial Intelligence” itself. We create revolutionary tools and then slap on labels that are either too broad, too narrow, or just plain confusing. Right now, this exact problem is hitting its peak with the term dominating every headline: “AI”. The truth is, bundling everything under this single umbrella is causing a fundamental misunderstanding of two very different technologies with vastly different business outcomes.

It all starts with prediction

At its core, all modern AI, whether it’s called predictive or generative, is built on the same foundation: using massive amounts of data and machine learning to recognize patterns and make a prediction.

It’s a principle we live and breathe at Pobuca since 2018. Our churn prediction algorithm is a classic example of what we should call Predictive AI. It sifts through vast amounts of customer data—interactions, transactions, support tickets—to identify the subtle patterns that signal a customer might be about to leave. Its job is to predict a specific, narrow outcome.

Now, here’s the twist. The powerful Generative AI models that are capturing the world’s imagination, like those from OpenAI, Google, or Anthropic, operate on the very same principle.

When you ask ChatGPT a question, it is, in a highly sophisticated way, just predicting the next most probable word, and then the next, and the next, based on the patterns it learned from training on nearly the entire internet.

Same principle, radically different outcomes

So, the underlying tech is similar. But the business outcome? They exist in completely different universes.

• Predictive AI is like a high-powered scalpel. It’s designed to solve a specific, narrow problem with incredible precision—predicting churn, identifying fraud, or optimizing a supply chain. It makes a process smarter.
• Generative AI is like a creative partner with access to all of human knowledge. It doesn’t just solve a narrow problem; it emulates the human brain’s ability to synthesize information, create new content, and reason through complex tasks. It creates new things—text, images, code, and strategies.

We, the people of technology, have put the cart before the horse. Because the foundational tech has shared principles, we’ve grouped them together. But in doing so, we’ve ignored the monumental difference in what they actually do for a business and for the world.

Stop lumping them together!

Unless you are having a deeply technical discussion with data scientists, there is no good reason to treat Predictive AI and Generative AI as one and the same. Lumping them into the single term “AI” is a mistake because:

• Their real-world uses are fundamentally different.
• The problems they solve and the implications (ethical, operational) are worlds apart.
• The people who need to use and control them within an organization are often not the same.

Mixing them up just creates confusion and slows down adoption. And let’s be honest, technology has always been bad at naming things…

The Trillion-Dollar difference

Don’t just take my word for it. The economic data tells the same story of two distinct, powerful forces. A landmark report from McKinsey, “The economic potential of AI,” helps illustrate the scale.

While the numbers are often blended, the report suggests that Generative AI is projected to add between $2.6 trillion and $4.4 trillion in value annually to the global economy.

However, the impact of traditional AI analytics and Predictive AI is even larger. The same report estimates the total annual value created by all forms of AI to be between $11 trillion and $17.7 trillion.

This means that Predictive AI, the workhorse of the enterprise, accounts for the vast majority of AI’s current economic impact, making existing industries faster, smarter, and more efficient.

Beyond economics: A civilizational shift

But here is the most important distinction of all, and it has little to do with trillions of dollars.

Predictive AI, for all its power, is an economic booster. It will create enormous wealth and efficiencies, but it won’t fundamentally challenge our core economic model. It makes capitalism run better.

Generative AI is a civilizational shift.

Despite its current smaller economic footprint, its potential impact is cosmic.Why?

Because for the first time in the history of humanity, we are building non-biological entities that are on a path to being smarter than we are. We are creating tools that can not only perform but also innovate and strategize in nearly every mental task that was once the exclusive domain of humans.

Predictive AI helps a company win in the current market. Generative AI will force us to ask what the market of tomorrow even looks like. It will challenge the very definition of work, creativity, and value, forcing a conversation about what comes after capitalism as we know it.

Understanding this distinction isn’t just a matter of semantics. It’s the key to navigating the next century.