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Since AI videos and ChatGPT became general campfire talk, the expert debate has fractured. Books like Reid Hoffman and Greg Beato's "Superagency" cover these different schools of thought in great detail, but for this piece, I want to focus on the two reactions I see most often in the broader public. On one side are the amazed, who believe they're seeing true intelligence; on the other are the skeptics, who dismiss it as a trick because it isn't, and can never be, human.

My question is simply this: does it have to be either?

Both views are a trap, born from our obsession with measuring AI against ourselves. We are trying to compare apples with apples, when we might be the vegetable. Since all technology extends our abilities, with AI we've simply created a version that is, in many ways, already more capable.

It all starts with a disarmingly simple concept: training. It’s a process I’ve used many times, and it's fundamentally human. Think of how a child learns what a "dog" is—not from a definition, but from countless examples. In the same way, show a model thousands of "B"s and it learns to read, or millions of cats and it learns "catness," unlocking image recognition. The large language models powering ChatGPT do the same with language, breaking sentences down into 'tokens' to learn the patterns of communication.

And let's be clear about "intelligence," as it's a loaded word. In biological brains, motivation is intrinsic. For programs, purpose is extrinsic—a goal defined by its creators, pursued with statistical efficiency. While purpose and motivation exist in both, they manifest in fundamentally different ways.

Now, for a dose of reality. I am not saying for a moment that this intelligence is aware or capable of genuine reasoning. My day job would be much easier if it was. These are closed-loop systems, but their isolated nature is an accidental limit—one we are rapidly erasing. From my professional experience, I can tell you the primary drive in the industry is to create a hyper-connected AI, integrating these models with all our devices, data, and services.

When you combine this learning mechanism, a massive dataset, and this new, actively connected capability, you don't get a synthetic human. You get something new.

Therefore, the conversation has to change. What matters is not what AI will become, but what it already is. We are living with the consequences of having already created a powerfully new kind of intelligence. This isn't a story about us being "stupid" or a machine becoming "smarter"—that's a distraction. The reality is that we are living with a mind that is fundamentally different from our own, and in some ways, that makes it even scarier. We must shift our focus from speculative futures to the practical, societal realities of the intelligence that is already here.