Forget the hype. Let’s talk about what AGI really is, and a simple way we might be able to spot it in the wild.

Let’s talk about AGI. It’s a term that gets thrown around a lot, usually followed by images of sci-fi robots or world-changing super-intelligence. But if you stop and ask someone to explain what is AGI (Artificial General Intelligence), the definition often gets a little fuzzy. Is it just a smarter Siri? Is it self-aware? It’s easy to get lost in the hype.

So, I’ve been thinking about a simpler, more down-to-earth way to look at it. Forget the killer robots for a second. Let’s just have a friendly chat about what AGI might actually look like and how we could ever know if we’ve found it.

So, What is AGI, Really?

At its core, “general” intelligence is the key. Most AI today is incredibly specialized. You have an AI that can master the game of Go, another that can write code, and another that can create beautiful images from a text prompt. They are amazing at their one thing, but they are one-trick ponies. You can’t ask the Go-playing AI for a dinner recipe.

AGI is different. It’s about having the flexibility to learn and solve problems across many different domains, just like a human. Think of it like a Swiss Army knife versus a single screwdriver. The screwdriver is perfect for its one job, but the Swiss Army knife can handle a whole range of unexpected problems. An AGI wouldn’t need to be pre-trained on every single task in the universe. Instead, it would have the underlying ability to figure things out, connect ideas, and apply knowledge from one area to another. For more on the technical definitions, you can check out how major tech players like IBM define Artificial General Intelligence.

A More Personal Definition of AGI

Here’s a thought experiment I find helpful. Imagine an AI that could do your entire job. Not just the repetitive parts, but all of it. The creative problem-solving, the tricky client emails, the strategic planning—everything. And it could do it faster, cheaper, and maybe even better than you.

From a personal point of view, that’s when you’ve been “AGI’d.”

When an AI reaches that level of capability for your specific role, it has achieved AGI for that work. Now, zoom out. Imagine that same capability spreading across an entire profession, like accounting or graphic design. Then zoom out further to an entire industry. That’s the bigger picture. It’s not one single moment where a light switches on and “AGI is here.” It’s more likely to be a gradual process where AI becomes generally capable in more and more complex domains, until it matches or exceeds human flexibility.

The Big Question: How Could We Create an AGI Test?

Okay, so if we have a clearer idea of what we’re looking for, how do we test for it? This is the tricky part. You can’t just give an AI an IQ test. The real challenge is proving true understanding and the ability to learn independently.

This is where a simple, practical test comes to mind. Let’s call it the “Indie Study Test.”

The idea is simple: can an AI teach itself a complex new skill in the same way a human would? Not by being fed a perfectly curated dataset of a million examples, but by engaging with messy, real-world learning materials.

Here are a couple of fun examples of what this AGI test could look like:

• Learn a Niche Language: Give the AI access to the internet and tell it to learn a less-common programming language like Haskell or Lisp. It would have to find the documentation, read through tutorials on blogs, watch video lectures, and then actually start writing code, debugging its own errors, and building a small project. The same process a human developer would go through.
• Become a Critic: Ask the AI to read a popular manga series, then watch its anime adaptation. The final task? Write a detailed analysis of the adaptation, explaining the differences in pacing, what plot points were changed, and how the tone shifted from page to screen. This requires comprehension, context, and comparative analysis—not just data processing.

This is a far cry from the AI we have today. The path to AGI is filled with immense challenges, as many researchers at places like MIT point out. But this kind of test feels more meaningful than a simple Q&A. It tests for the ability to learn, adapt, and synthesize information from disparate sources.

We’re still a long way off, but thinking about it this way helps cut through the noise. AGI isn’t just about raw power; it’s about flexible, independent learning. And that’s a much more interesting future to imagine.