> achieve AGI

They might as well invest in cold fusion, warm superconductivity, curing cancer or whatever sci-fi concept you have.

Were you not paying attention? Modern LLMs are incredibly general AIs, and nearing human or superhuman performance in many areas. And the tech just keeps advancing.

If you don't see "AGI is possible", you aren't looking.

There is a very big difference between language generation and true intelligence.

"The ability to speak does not make you intelligent." - Qui Gon Jinn

Oh, funny that you say that.

Modern LLMs show empirically that the "difference" is fuzzy at best. You can try to draw a distinction. You can try to draw the lines in the sand and define "true intelligence" in a way that would include humans but exclude "false intelligence" of Mythos 5. An exercise in vanity, in my eyes.

Or you can go with "any sufficiently advanced language generation is indistinguishable from intelligence" and drop the matter. My advice is to do exactly that.

We used to think that if a computer could play chess, it would be intelligent. Maybe back then they were also people saying "stop trying to draw lines, just admit it's intelligent!!" Good thing we didn't listen to them.

The act of distinguishing between human intelligence and LLMs is what allows us to figure out how to make it better. There are still some deep limitations, and to ignore them is a mistake. That doesn't take away from how crazy good they are.

Right. It's actually amazing that large language models can be as effective as they are, given that at their core they are simply matching up word-frequency patterns. But with a large enough context and enough parameters, those word-frequency patterns actually do a decent job of simulating intelligence: I'm able to give rather ambiguous instructions to Claude Code (like "go back to the suggestion you made a while back about (foo) and explain in more detail what the benefits and drawbacks of that approach would be"), and it is able to look through its context, find the part where it suggested (foo), and expand on its suggestion. This is a qualitative difference in human-computer interaction: I can type instructions that are very similar to what I would say to another human being, rather than having to be utterly unambiguous the way you have to be in writing code. It's also good at synthesizing information faster than I could: these days instead of searching MSDN for some obscure API method, I ask Claude "what's the syntax to create a foo from a bar?" and it finds me the MakeBarIntoFoo method faster than I would have (especially because I would have started with CreateFooFromBar and not found it).

But I never forget that it's a simulation of intelligence. I use it for the things it's trained on (generating code) and I don't expect the model to be good at writing poetry, or fiction. Nor do I expect it to have any actual understanding of the things it is actually trained on. Modern models are pretty good at simulating understanding, but even so they will still produce things that a human being would immediately know is wrong, e.g. image-generation models producing hands with the wrong number of fingers, or a person with three arms, or whatever. Those happen less and less often as models have been better trained (and I bet that verification steps are happening behind the scenes to catch and discard some of the classic mistakes), but they still happen.

It's the dancing bear, except this bear is actually managing some really spectacular dance moves. Some of the time. Other times it falls flat on its face. But it's really, really impressive that the bear is actually managing to dance so well.

We see in practice that the last to adopt technology do in fact have newer tech than the ones who started it. IE 3G in Asia vs land lines in USA.

So all they have to do is wait a bit and build a frontier model of their own.

I don't think it works that way for AI. Comms networks are extensive physical infrastructure that, by definition, has to cover ground. AIs cover the ground by riding the existing comms lines.

In telecomms, the late mover has the advantage of not having legacy networks to maintain, and having better technologies available at rollout time. What is the "late mover advantage" in AI? Being able to distill from every bleeding edge frontier lab? That gets you near parity at best.

> That gets you near parity at best.

So they don't spend 20 trillion dollars to achieve AGI, and in the end they still end up at parity for a tiny fraction of the cost.

How can you claim that this isn't a win?

That assumes they release their models publicly. The future is leaning towards these labs air gapping their best stuff (Model 2, etc) and using it internally to snipe their competitors and charge insane amounts for monitored use in consulting environments.

You can't distill or catch up if you can't access the models. You'll basically have a situation where nation-states will need to try and steal the models Oceans 11 style.

Getting to parity with less money means that you can go further with the same amount of money.

Norway doesn't have to fund future capex just like OpenAI doesn't have to fund future capex. They both borrow the capital.

When I think of Norway, the first thing that comes to mind is its willingness to commit to huge future capex spending. Not to mention, selling more than half its equities in order to buy one risky asset totally aligns with its investment strategy and, you know, having to pay pensions, other minor concerns like that.

In all seriousness though it is pretty interesting to consider if it really happened, a sovereign wealth fund pivots to the crazy high risk strategy, writes a blank check and tries to actually win. If it works does that country just become the rulers of the world?