Why I Call It "Intelligence Augmentation," Not "Artificial Intelligence"

The term "artificial intelligence" is a lie.

Not in the sense that these tools don't work. They obviously work. I use them every day. My entire team uses them. But the phrase itself creates expectations that don't match reality, and those false expectations lead to bad decisions, wasted resources, and a fundamental misunderstanding of what we're actually building.

I started calling it "Intelligence Augmentation" instead. Not as a branding exercise or a hot take, but because that's what it actually is.

The Aha Moment

I was refining my portfolio site, stuck in this conceptual middle ground. I'd been pushing back against AI maximalism (the "this changes everything, nobody will have jobs" crowd) but I also rejected AI minimalism, the dismissive "it's just autocomplete" take. There's a middle path, I kept saying. A more nuanced view.

But "middle path" was still defining the territory in terms set by other people. I was positioning myself between two poles instead of questioning whether those poles made sense at all.

Then it clicked.

This isn't artificial intelligence. It's something different. It's a tool that expands my own intelligence by arming me with details I'd have to look up, patterns I'd have to remember, boilerplate I'd have to write. It augments what I can already do. It doesn't replace what I can't.

What Augmentation Actually Looks Like

The developers I work with who use these tools well, they're all doing the same thing. They use generative models to look up syntax, generate boilerplate, make connections between patterns, solve algorithms. It's like a supercharged search engine. Stack Overflow with the best possible interface, one that can apply general solutions to specific use cases.

But here's the critical part: they validate everything.

If you don't already know what you're doing, the tool can't do it for you. It can enhance skills you have. It can help you learn faster by surfacing examples and explanations. But you still have to exercise judgment. You still have to discern whether the output makes sense.

Generative models have a hard time recognizing when they're spitting out nonsense. They can recognize patterns that aren't there. They can tunnel on the wrong part of a pattern. Pattern matching without the ability to recognize whether the pattern actually fits, that's not intelligence.

Why "Artificial Intelligence" Causes Problems Every Time

Calling it artificial intelligence frames every discussion in a way that makes people think it can do things it can't do.

People hear "intelligence" and assume it can make decisions. But if a decision is narrow enough that an AI could reliably make it, you could probably handle it with traditional if-then logic. You don't need a generative model for well-defined categories with clear boundaries.

What these tools actually do is add context for human decision-making. They surface information, generate options, make connections. The human still has to decide.

I was in a meeting once with a company that wanted to build an autonomous AI system for oil lease payments. They'd trained a model to read documents, figure out who owned what percentage of a lease, calculate payments, and cut checks. Completely automated.

I had to stop them.

"What happens when the AI hallucinates?" I asked. "It's going to read a document, see something that isn't there, and cut the wrong check. Then you'll have to walk it back. Pull money out of someone's account. Reissue a payment. Who's at fault when that happens?"

They weren't happy with that question. They kept saying they were "training their own model," which really meant they'd been feeding context into an AI with memory. They weren't training anything.

"People don't play around with their money," I said. "You can't tell them 'sorry, the LLM made a mistake, we'll fix it in 60 days.' That's not going to fly."

They pushed back: "Well, 90% of the time it's cut and dry."

"Then in 90% of cases, you just need a decision matrix running on traditional code. You don't need AI for that. For the other 10%, you need a human reviewing the AI's research, checking for errors, and taking responsibility if they miss something."

We didn't get that contract.

The Photo Booth Vindication

I was using a generative model to clean up old photos for my site. I fed it some images and prompted it to make them look like portraits.

It generated a completely random picture of two guys in a photo booth. Nobody I knew. Nothing related to my images. Just two random dudes.

I laughed so hard my sides hurt.

This was it. This was the proof. The model looked at a pattern, generated an output, but had zero capability to recognize that the output didn't fit. No ability to say "wait, this doesn't make sense."

I tweaked the prompt, adjusted the seed images, ran it again. Perfect output.

The randomness isn't a bug you can patch out. Some particular combination of inputs activates a pathway in the neural network, and you get complete nonsense. You can add guardrails and layers (that's how we got to current LLMs and image generators) but the possibility of bizarre outputs never goes away.

What This Changes About How I Build

This shift in thinking completely changed my approach to building with these tools.

My company was pushing for autonomous AI systems that make decisions. I'm deeply skeptical of that. Low-value decisions might be automatable with generative models, but any high-value decision needs a human in the loop. For two reasons:

One: The random photo booth problem. You can't guard against weird outputs, no matter how many layers you add.

Two: Someone has to be responsible when outcomes go wrong. You can't point to an algorithm and say "the AI did it."

So I focus on human-in-the-loop designs. How does this tool make someone's job better, easier, faster? How does it free them up for higher-value work? How can we eliminate low-level tasks that an AI can handle with supervision?

Throughout 2024 and 2025, the conversation was all about eliminating redundancies and replacing jobs. Now I'm seeing the conversation shift. People are realizing that augmentation (making workers more capable) is the sustainable path.

What Intelligence Actually Is

The term "artificial" is wrong because these models aren't fake. They use natural statistical methods, sophisticated algorithms that create large models through processes similar to how programmers write programs: taking results, refining, iterating. Artificial in the sense of "not biological," sure. But not artificial in the sense of "fake."

The term "intelligence" is wrong because pattern matching isn't intelligence on its own.

Intelligence is the ability to match a pattern and recognize that the pattern fits. To make that discernment: I can't even fully describe it. Nobody can effectively describe the feeling of knowing when a pattern fits. When you've made an intuitive leap, a logical deduction, or connected ideas and thought "yes, that's it, that's real."

That recognition, that understanding that the pattern fits, that's intelligence. Generative models don't have it.

And we struggle to articulate the process of that recognition even in ourselves. Which makes it nearly impossible to build it into a model.

The Framework That Actually Works

Intelligence Augmentation as a framework:

Augmentation surfaces information. It doesn't make decisions. It provides context, generates options, recalls patterns, and presents possibilities.

Augmentation requires expertise. If you don't know what you're looking at, you can't tell good output from garbage. The tool enhances what you already know.

Augmentation keeps humans responsible. Someone has to be accountable for outcomes. That someone can't be an algorithm.

Augmentation frees up capacity. The goal isn't replacement. It's making people more capable by handling the low-level work that bogs them down.

This isn't a middle path between AI maximalism and minimalism. It's a different path entirely. One that acknowledges what these tools actually do instead of projecting onto them what we wish they could do.

The language matters. Call it what it is.