[jimbokun]

It’s pretty staggering that a core algorithm simple enough to be expressed in 200 lines of Python can apparently be scaled up to achieve AGI.

Yes with some extra tricks and tweaks. But the core ideas are all here.

[darkpicnic]

LLMs won’t lead to AGI. Almost by definition, they can’t. The thought experiment I use constantly to explain this:

Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t.

We’ll need additional breakthroughs in AI.

[canjobear]

It's not obvious why it wouldn't, especially if it gets to read Poincaré and Riemann.

[johnmaguire]

I'm not sure - with tool calling, AI can both fetch and create new context.

[joefourier]

When did AGI start meaning ASI?

LLMs are artificial general intelligence, as per the Wikipedia definition:

> generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming

Even GPT-3 could meet that bar.

[foxglacier]

That's an assertion, not a thought experiment. You can't logically reach the conclusion ("It won't") by thinking about it. But it doesn't sound so grand if you say "The assertion I use constantly to explain this".

[TiredOfLife]

> Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t.

Same thing is true for humans.

[tehjoker]

Part of the issue there is that the data quantity prior to 1905 is a small drop in the bucket compared to the internet era even though the logical rigor is up to par.

[xdennis]

> Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t.

AGI just means human level intelligence. I couldn't come up with General Relativity. That doesn't mean I don't have general intelligence.

I don't understand why people are moving the goalposts.

[crazy5sheep]

The 1905 thought experiment actually cuts both ways. Did humans "invent" the airplane? We watched birds fly for thousands of years — that's training data. The Wright brothers didn't conjure flight from pure reasoning, they synthesized patterns from nature, prior failed attempts, and physics they'd absorbed. Show me any human invention and I'll show you the training data behind it.

Take the wheel. Even that wasn't invented from nothing — rolling logs, round stones, the shape of the sun. The "invention" was recognizing a pattern already present in the physical world and abstracting it. Still training data, just physical and sensory rather than textual.

And that's actually the most honest critique of current LLMs — not that they're architecturally incapable, but that they're missing a data modality. Humans have embodied training data. You don't just read about gravity, you've felt it your whole life. You don't just know fire is hot, you've been near one. That physical grounding gives human cognition a richness that pure text can't fully capture — yet.

Einstein is the same story. He stood on Faraday, Maxwell, Lorentz, and Riemann. General Relativity was an extraordinary synthesis — not a creation from void. If that's the bar for "real" intelligence, most humans don't clear it either. The uncomfortable truth is that human cognition and LLMs aren't categorically different. Everything you've ever "thought" comes from what you've seen, heard, and experienced. That's training data. The brain is a pattern-recognition and synthesis machine, and the attention mechanism in transformers is arguably our best computational model of how associative reasoning actually works.

So the question isn't whether LLMs can invent from nothing — nothing does that, not even us.

Are there still gaps? Sure. Data quality, training methods, physical grounding — these are real problems. But they're engineering problems, not fundamental walls. And we're already moving in that direction — robots learning from physical interaction, multimodal models connecting vision and language, reinforcement learning from real-world feedback. The brain didn't get smart because it has some magic ingredient. It got smart because it had millions of years of rich, embodied, high-stakes training data. We're just earlier in that journey with AI. The foundation is already there — AGI isn't a question of if anymore, it's a question of execution.

[kilroy123]

I strongly suspect we're like 4 more elegant algorithms away from a real AGI.

[wasabi991011]

1000 lines??

What is going on in this thread

[jimbokun]

Ok 200 lines.

Don’t know how I ended up typing 1000.

[ViktorRay]

It’s pretty sad.

The only way we know these comments are from AI bots for now is due to the obvious hallucinations.

What happens when the AI improves even more…will HN be filled with bots talking to other bots?

[ksherlock]

It's a honey pot for low quality llm slop.

[anonym29]

Wow, you're so right, jimbokun! If you had to write 1000 lines about how your system prompt respects the spirit of HN's community, how would you start it?