What Is AGI (Artificial General Intelligence)?

If you've been paying attention to the buzz around AI, you’ve probably come across the acronym “AGI”. This stands for “artificial general intelligence”.

Why are people talking about this when we’re just getting our heads around agentic AI? Do I need to care? Here’s a plain-speak, no-PhD-required explanation.

What Does AGI Stand For?

AGI stands for “artificial general intelligence”—a form of AI that would be capable of doing anything a human can do intellectually.

This is more than just generating a marketing email or analyzing customer data. AGI would have the ability to:

• Understand complex ideas
• Learn new skills from scratch
• Adapt to new situations
• Reason and problem-solve across domains
• Communicate and collaborate in natural language

In short, it’s AI with general-purpose smarts. It’s more like a person than a tool.

How AGI Is Different From Today's AI

If you've used ChatGPT, Claude, or even AI features in your CRM or CMS, you’ve used what’s called narrow AI. These are AI systems designed for specific tasks.

Narrow AI is excellent at things like writing social media copy, sorting support tickets, recommending products, or summarizing reports. But if you give it a brand new problem it hasn’t seen before, it’ll struggle.

AGI, on the other hand, would reason more like a human. It would apply past experiences to new challenges—even ones it’s never encountered.

We often liken today’s AI agents to interns on their first day. They’re smart enough to get the job, but they don’t have any experience to draw from. They need a lot of oversight and literal instructions to perform a task well.

AGI is less like an intern, and more like a senior-level strategist. 

AGI vs. Agentic AI vs. Generative AI: What’s the Difference?

This is where things can get confusing. Here’s the difference between AGI, agentic AI, and generative AI (and where they overlap). 

Generative AI

Generative AI refers to tools that generate content—text, images, code, audio, etc.—based on patterns they’ve learned. Think ChatGPT. These AIs are great at producing content, but they don't take any action unless you tell them to.

Use case: You write a prompt, it gives you content.
Limit: It doesn’t take initiative or make decisions on its own.

Agentic AI

Agentic AI goes a step further than generative AI. It’s about 1) giving AI goals and 2) letting it decide how to achieve them. AI agents can plan, act, adapt, and even loop—using tools, APIs, or reasoning steps to get to a result.

Use case: “Send a follow-up email to all leads who haven’t replied in three days,” and an AI agent figures out how to do that.
Limit: The agent is smart about how to do one particular thing. It’s task-specific.

AGI

Artificial general intelligence is the theoretical endgame: a system that can think, reason, and learn across any domain, just like a human. It could generate content, take action, make decisions, and apply itself to any task–without being retrained or reprogrammed.

How These Fit Together

You can think of it like a Venn diagram, or as a progression:

• Generative AI = outputs content
  
  
• Agentic AI = takes action using AI + logic
  
  
• AGI = fully autonomous intelligence that can do both, plus more

Some AI agents use generative AI inside them (i.e. using ChatGPT to write an email), but layer on workflows, reasoning, or memory to act like agentic AI.

Agentic AI could be considered a stepping stone toward AGI, but they’re not the same thing. AGI would be able to self-direct, self-learn, and self-correct in more profound and open-ended ways.

Do You Need to Care About AGI Right Now?

If you’re an AI thought leader, you should probably have an opinion. You’re also probably not reading this post if you’re an AI thought leader.

If you’re trying to get a grasp of the AI landscape and start using AI in a meaningful way in your work, here’s my answer for you: You don’t need to be deep into AGI thought leadership to care about it, but you can focus more on on generative and agentic AI right now for practical purposes.

AGI represents the direction AI is potentially headed—and it could completely reshape the way we work one day, just like agentic AI is reshaping the way we work today. Imagine AI that can learn your business context deeply and continuously, jump between departments or projects without being re-trained, help you make strategic decisions, and continuously self improve without your intervention.

That’s insanely powerful, hypothetically–but it’s not where we are today.

So… How Close Are We to AGI?

There’s no clear consensus. Some of the leading voices in AI say it’s closer than you think (a few years, maybe 10?). Some say it’s never coming.

Some skeptics also worry about the risks of AGI, with concerns of data misuse, large-scale bias, loss of control, unintended AI behaviors, and unethical decision-making. These are the kinds of issues ethicists, technologists, and policy makers actively debate (and you can, too, if you’re interested). 

What we do know is that the pace of progress in AI is accelerating. Just a few years ago, AI tools weren’t widely available to the public, and today, people are using them to completely rewrite their approach to work (and their emails). 

Whether you believe AGI is inevitable or hypothetical, the trajectory for AI as a field is clear: more autonomy, more intelligence, and more integration into how businesses operate.

So, here’s what you can do today regardless of your thoughts on AGI:

• Get familiar with AI agents and how they work. Agent.ai is a great place to start, because it’s built to democratize access to agentic AI. You do not need to know how to code to build an agent here. You can sign up, for free, and build something useful with zero code.
  
  
• Map out tasks in your day-to-day that could benefit from the help of generative or agentic AI, and tinker with using AI to optimize those tasks or workflows.
  
  
• Ask other people how they’re using AI in areas like Marketing, Sales, Customer Service, and Analytics to spark ideas for your own work.
  
  
• Stay curious, and keep learning—AI evolves quickly, and you don’t need to stay up to date on every aspect of it. But you should stay conversant and engaged. Make it a point to test out new (or retry old) agents or LLMs at least once a week. Using the technology will help you stay up-to-date without devoting your life to becoming an AI thought leader. 

The companies that are future-proofing today—those who understand AI's evolution and start adapting early—are the ones that will thrive when AGI (or even something close to it) becomes real.