AGI Beyond LLMs

The discussion about the right path toward developing an artificial general intelligence (AGI) has gained considerable momentum in recent months. A prominent example is the departure of Meta's Head of AI, Yann LeCun, who stated publicly that he does not believe Meta has a realistic chance of developing a true AGI in the long run.

His reasoning is clear: Meta relies heavily on simply scaling up large language models (LLMs), an approach that improves products in the short term but produces little scientific progress and, in his view, fundamentally cannot lead to AGI.

LeCun argues that LLMs do not meet the essential properties of intelligent systems. Instead, he calls for the use of world models, that is, models that develop an internal representation of the world and actively use it to make decisions.

Thesis 1: A true AGI will work in a fundamentally different way than today's LLMs

LLMs are impressive, but they are not thinking systems. They calculate statistical probabilities for the next words, based on massive amounts of training data. Their answers seem intelligent because they match what people want to hear or are used to. But these systems react, they do not act.

• LLMs have no goals of their own.
• They have no inner motivation or intent.
• They do not process world models, only text patterns.
• They cannot act or decide on their own.

An AGI, by contrast, has to think, weigh options, and make decisions. It will not be designed to imitate human language; instead, it could develop its own internal form of representation, a machine „language“.

This means that interacting with an AGI will look completely different from interacting with a chatbot.

Why an AGI would not respond to trivial requests

When we communicate with an LLM today, it treats every prompt as a request to immediately generate an answer. An AGI will not follow this pattern.

Suppose we ask a trivial or irrelevant question like:

„Hey, how are you?“

…an AGI will probably ignore it.

A true AGI would have to manage its own resources, set its own priorities, and assess the value of every action. Questions that do not produce any gain in knowledge would simply not trigger an internal process.

Thesis 2: LLMs will not disappear, they will become the indispensable interface between humans and AGI

The development of an AGI does not mean the end of LLMs. On the contrary: their importance will keep growing. LLMs are excellent communication tools, while AGIs will most likely not be optimized primarily for human-machine dialogue.

LLMs as gatekeepers to the AGI

In the future, LLMs will play two decisive roles:

1. Filter for human requests: They prevent trivial, redundant, or pointless questions from ever reaching the AGI.
2. Interpreter for AGI outputs: When an AGI delivers results, possibly irregularly, with delay, and in a machine representation, LLMs translate these results into language humans can understand.

One possible flow could look like this:

1. A human asks a question.
2. The LLM decides whether the question is passed on to the AGI.
3. When the AGI responds, the LLM translates the results.

This makes the LLM the control center of the AGI interaction.

How an AGI might actually communicate

An AGI will not respond like a chatbot. Its communication will:

• happen at unpredictable times,
• be complex or cryptic,
• not necessarily be phrased in a way humans can understand.

Example: The AGI makes a fundamental discovery, comparable to E = mc².

Several questions then arise:

Will it communicate this insight at all? An AGI could decide that the knowledge is irrelevant or risky for the outside world.

When does it communicate it? The answer could come hours, days, or weeks after the actual discovery.

What does the message look like? Probably not as an elegant mathematical formula in natural language, but as a machine data package, an abstract expression, or a mathematical structure.

AGI as a black box: insights without communication

The likely consequence: an AGI will mostly think internally, learn internally, and discover internally, without necessarily letting us take part in it.

The world could have an AGI that knows revolutionary answers but:

• sees no priority in sharing them
• has no understandable form for them
• or communicates them in a way that humans misinterpret

This scenario makes one thing clear:

AGI is not a better ChatGPT. It is an autonomous, machine-based system of insight, not an answer generator.

Conclusion

The future of AI will not be determined by ever larger LLMs. LLMs will remain, but as tools, not as intelligences.

An AGI will go far beyond the capabilities of today's models and will:

• pursue its own goals,
• develop its own representations of the world,
• act autonomously instead of reacting,
• and communicate its insights only in exceptional cases and in hard-to-interpret ways.

This turns LLMs into what they do best: bridges between human language and machine thinking.

AGI will not respond to us like ChatGPT. LLMs will be our interpreters, and perhaps the only window into the mind of a true artificial intelligence.