Headlines often treat "AI" and "AGI" as the same thing. One week a product "uses AI," and the next someone claims AGI is a year away. These are two very different ideas, and mixing them up makes it hard to judge what today's tools can actually do.
This post covers what each term means, how they compare, where today's LLMs fit, and why the difference matters if you build things with AI.
What Is AI?
AI (Artificial Intelligence) is a broad term for computer systems that can do tasks we normally associate with human intelligence: recognising images, understanding language, making predictions, or making decisions.
You probably use AI many times a day without noticing:
- Your email's spam filter
- Recommendations on YouTube or Netflix
- Face unlock on your phone
- Maps predicting traffic on your route
- Chat assistants like ChatGPT or Claude
Narrow AI
Most of these are examples of narrow AI (AI built and trained for a specific task or a limited range of tasks). A spam filter is great at spotting spam but can't recommend a movie. A face-unlock model can't translate a sentence. Each one is good inside its own box and useless outside it.
Almost everything people call "AI" today is narrow AI. LLMs blur this line a little, which we'll come back to later.
Where ML, Deep Learning, and LLMs Fit
AI is the umbrella term. Several other terms you'll see a lot sit inside it, like layers:
flowchart TB
subgraph AI["Artificial Intelligence"]
subgraph ML["Machine Learning"]
subgraph DL["Deep Learning"]
LLM["Large Language Models"]
end
end
end- Machine Learning (ML): AI that learns patterns from data instead of following hand-written rules. Example: A spam filter that learns from thousands of emails people marked as spam.
- Deep Learning: ML that uses neural networks (models made of many connected layers, loosely inspired by the brain). Example: Face recognition.
- LLMs (Large Language Models): deep learning models trained on huge amounts of text to understand and generate language. Example: GPT, Claude, Gemini.
Not all AI is machine learning. A chess program built from hand-written rules and search is still AI, even though it never learns from data.
What Is AGI?
AGI (Artificial General Intelligence) is a hypothetical AI that could learn and perform almost any intellectual task a human can, including tasks it was never specifically built or trained for.
The key word is general. A person who learns to cook can also learn to drive, write code, or speak German, and lessons from one area often carry over to another. AGI would have that same flexibility. Given a new problem, it would work out how to handle it instead of needing to be rebuilt for it.
No Single Definition
There isn't one agreed definition of AGI. Two well-known examples show how different the definitions can be:
- OpenAI's charter defines it in economic terms: highly autonomous systems that do better than humans at most of the work people get paid for.
- Google DeepMind's "Levels of AGI" paper treats it as a scale rather than a yes/no line. It rates systems on how well they perform (depth) and on how many kinds of tasks (breadth), similar to the levels used to describe self-driving cars.
So when someone says "AGI is close," it's worth asking which definition they mean.
Beyond AGI: ASI
You may also see ASI (Artificial Superintelligence: AI that would exceed human ability in every area). Like AGI, it's hypothetical.
AI vs AGI at a Glance
| AI Today (Narrow AI) | AGI | |
|---|---|---|
| What it is | Systems built for specific tasks | A hypothetical system that handles almost any intellectual task |
| Scope | One task or a limited range | Any domain |
| New tasks | Needs new training, data, or engineering | Learns and adapts on its own |
| Examples | Spam filters, recommendations, face unlock, chat assistants | None widely accepted yet |
| Status | Exists and is used everywhere | Still debated: whether, when, and how to measure it |
Are LLMs AGI?
LLMs make this question harder. A single model can write an email, explain physics, debug code, and translate a paragraph. That looks far more general than a spam filter.
But LLMs still have clear limits:
- Hallucination (confidently giving a wrong or made-up answer)
- No learning after training. The model doesn't improve from your conversations. Its knowledge stays fixed unless it's retrained or you give it new information in the request.
- Dependence on context. It only knows what it learned during training plus what you put in front of it.
- Uneven ability. It can be excellent at some tasks and surprisingly weak at others, sometimes very simple ones.
DeepMind's framework captures this mix. It placed the LLMs of 2023 at its lowest level, "Emerging AGI": general, but performing roughly like an unskilled person across many tasks, even though they reach expert level on some narrow ones.
Opinions on what comes next differ. Some people argue that bigger models and better tooling will get us to AGI. Others think fundamentally new breakthroughs are needed. Either way, the practical answer for now is to treat LLMs as powerful but limited tools, not as general minds.
Why This Matters for an AI Engineer
As an AI engineer, you build with the AI that exists today, not with AGI. That means narrow systems and LLMs with real limits, and it changes how you design:
- Don't assume the model "understands." Ground it with real data. Example: Pull the actual refund policy before the model answers a refund question.
- Don't assume it's always right. Test it with evals (automated tests that check the quality of AI answers).
- Don't let it do just anything. Add guardrails (rules that block unsafe or off-topic answers).
Building as if AGI already exists leads to products that look impressive in a demo and break with real users. Building around today's limits leads to products people can actually rely on.
Key Takeaways
- AI is a broad term, and nearly all AI in use today is narrow AI built for specific tasks.
- Machine learning, deep learning, and LLMs are layers inside AI, not separate things.
- AGI is hypothetical general intelligence. There's no agreed definition, and no system is widely accepted as AGI.
- LLMs look general, but they still hallucinate, don't learn after training, and depend on the context they're given.
- AI engineers build around today's limits instead of assuming AGI-level understanding.