Many users fail to even grasp the broader concept of using AI in the context of idea generation and brainstorming. When used correctly—and assuming an exchange in which the user is an expert with no particular resistance to challenging their own assumptions—AI models can act, in fact, as a kind of cognitive accelerant.

From the perspective of AI, we can imagine this as a fusion between an AI and its user—some form of integrated partnership. Think of Cortana in Halo or the Major in Ghost in the Shell. While it might take a while before we’re truly in the realm of holographic neural interfaces, it’s entirely possible that we’re, at this very moment, heading unwittingly in that direction.

You see, assuming your competence in a given field is strong enough to recognize logical patterns and underlying assumptions, then having access to a capable LLM willing to provide information is akin to holding a key to the inner workings of your own thoughts. In general terms, we could refer to “an iteration” as a cycle in which one of our ideas is challenged, refined, and then re-expressed to the outside world.

Using AI as an integrated part of this refinement process allows you to move through multiple iterations of a given element at a pace previously unimaginable. We already have a proto-example of this when we use AI as a text corrector—a fairly common and straightforward use. A piece of writing that would normally take you hours to produce suddenly comes together faster and more smoothly, thanks to the model helping to “iron out” thoughts or ideas that might not yet be fully processed.

This results in a kind of expanded mental agility: an enhanced perception of the elements surrounding a particular topic, and a heightened ability to process those elements. It’s easy to see—when we ask the model about a particular topic, we activate informational patterns that align with what we want to explore. An expert can then more efficiently coordinate and determine which of those insights are applicable to the piece in question.

Of course, this does require that the user has at least a vague sense of what the core idea is that they want to express in order to retain ownership of the process and control of the final result.

The more iterations you refine an idea through, the more “cycles” you complete—typically increasing your output capacity in ways you might not have previously considered. As with anything, there’s an unfortunate series of possible side effects that we have to take into account, but these ultimately return control to the user.

For instance, regular use of AI as a cognitive accelerant may place you in a position of dependency on the tool, causing you to second-guess your own thought process as “incomplete” without a model to triangulate your ideas. We need to remember that this process mimics the natural process of thought exchange—what happens when we dare to enter conversation with a stranger. Our ideas, even though they should always remain open to challenge, are never truly finished. Therefore, the premise is flawed: you’re not “done thinking” just because the AI gives you a particular output—any more than you’d be after a deep conversation with a peer. Take from that what you will, as long as you understand there are always degrees of comprehension and are open to change.

The second issue is that you may find yourself in a situation where you must constantly expand and deepen your knowledge in certain areas in order to maintain your “edge” in relation to the model. This is especially important when exploring fields that evolve rapidly, such as technology or regulatory frameworks.

The final side effect is the danger of losing your voice in the ocean of thoughts and ideas generated within the model. Keep in mind: models are nothing but probability engines—statistical wonders. Even when used as a corrector, you’re simply aligning your expression with the average state of the language at the time of production.

True creativity, however, takes time to build. It involves outliers, human interpretation, and the willingness to push language into new forms or applications—outside the norm—in order to expand the very foundation that AI models themselves draw from. This is part of the reason why models collapse when they’re trained on their own output: the limits of what’s possible begin to degrade as the model essentially “averages” all available information into a predictable, self-referential loop.

We should hope that technology continues to improve our efficiency as it evolves. Personally, I don’t see the real value of AI in its capacity to produce drafts or generalized ideas, but rather in its ability to surface conflicting information—helping me refine and reshape my own thought patterns.