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Comment by wongarsu | Hacker News Reader

OpenAI has all the name recognition (which is worth a couple billion in itself), but when it comes to actual business use cases in the here and now Anthropic seems ahead. Even more so if we are talking about software dev. But they are valued at less than half of OpenAI's valuation

What is somewhat justifying OpenAI's valuation is that they are still trying for AGI. They are not just working on models that work here and now, they are still approaching "simulating worlds" from all kinds of angles (vision, image generation, video generation, world generation), presumably in hopes that this will at some point coalesce in a model with much better understanding of our world and its agency in it. If this comes to pass OpenAI's value is near unlimited. If it doesn't, its value is at best half what it is today

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> What is somewhat justifying OpenAI's valuation is that they are still trying for AGI.

And that's the dealbreaker for me since they've been so adamant on scaling taking them there, while we're all seeing how it's been diminishing returns for a while.

I was worried a few years back with the overwhelming buzz, but my 2017 blogpost is still holding strong. To be fair it did point to ASI where valuation is indeed unlimited, but nowadays the definition of AGI is quite weakened in comparison.. but does that then convey an unlimited valuation?

> What is somewhat justifying OpenAI's valuation is that they are still trying for AGI.

"AGI" is the IPO.

Obligatory reminder that today's so called "AGI" has trouble figuring out whether I should walk or drive to the car wash in order to get my dirty car washed. It has to think through the scenario step by step, whereas any human can instantly grok the right answer.

I just don't know how to engage with these criticisms anymore. Do you not see how increasingly convoluted the "simple question LLMs can't answer" bar has gotten since 2022? Do the human beings you know not have occasional brain farts where they recommend dumb things that don't make much sense?

> Do the human beings you know not have occasional brain farts where they recommend dumb things that don't make much sense?

Not that dumb, no. That's why it's laughable to claim that LLMs are intelligent.

I should note for epistemic honesty that I expected I would be able to come up with an example of a mistake I made recently that was clearly equally dumb, and now I don't have a response to offer because I can't actually come up with that example.

> Do the human beings you know not have occasional brain farts where they recommend dumb things that don't make much sense?

I completely agree. I'm ashamed to admit, I've actually walked to the car wash without my car on more than one occasion. We all make mistakes!

The idea/hope is that a video model would answer the car wash problem correctly. There are exactly the kinds of issues you have to solve to avoid teleporting objects around in a video, so whenever we manage more than a couple seconds of coherent video we will have something that understands the real world much better than text-based models. Then we "just" have to somehow make a combined model that has this kind of understanding and can write text and make tool calls

Yes, this is kind of like Tesla promising full self driving in 2016

What are you talking about? OpenAI's ChatGPT free tier (that everyone uses) answers this in the first sentence within a couple seconds.

"If your goal is to get your dirty car washed… you should probably drive it to the car wash "

That problem went viral weeks ago so is no longer a valid test. At the time it was consistently tripping up all the SOTA models at least 50% of the time (you also have to use a sample > 1 given huge variation from even the exact same wording for each attempt).

The large hosted model providers always "fix" these issues as best as they can after they become popular. It's a consistent pattern repeated many times now, benefitting from this exact scenario seemingly "debunking" it well after the fact. Often the original behavior can be replicated after finding sufficient distance of modified wording/numbers/etc from the original prompt.

I don't understand what occasional hiccups prove. The models can pass college acceptance tests in advanced educational topics better than 99% of the human population, and because they occasionally have a shortcoming, it means they're worse than humans somehow? Those edge cases are quickly going from 1% -> 0.01% too...

"any human can instantly grok the right answer."

When asking a human about general world knowledge, they don't have the generality to give good answers for 90% of it. Even very basic questions humans like this, humans will trip up on many many more than the frontier LLMs.

For example, I just asked ChatGPT "The boat wash is 50 meters down the street. Should I drive, sail, or walk there to get my yacht detailed?" and it recommended walking. I'm sure with a tiny bit more effort, OpenAI could patch it to the point where it's a lot harder to confuse with this specific flavor of problem, but it doesn't alter the overall shape.

This question is obviously ambiguous. The context here on HN includes "questions LLMs are stupid about, I mention boat wash, clearly you should take the boat to the boat wash."

But this question posed to humans is plenty ambiguous because it doesn't specify whether you need to get to the boat or not, and whether or not the boat is at the wash already. ChatGPT Free Tier handles the ambiguity, note the finishing remark:

"If the boat wash is 50 meters down the street…

Drive? By the time you start the engine, you’re already there.

Sail? Unless there’s a canal running down your street, that’s going to be a very short and very awkward voyage.

Walk? You’ll be there in about 40 seconds.

The obvious winner is walk — unless this is a trick question and your yacht is currently parked in your living room.

If your yacht is already in the water and the wash is dock-accessible, then you’d idle it over. But if you’re just going there to arrange detailing, definitely walk."

You can make the argument that the boat variant is ambiguous (but a stretch), it's really not relevant since the point was revealing the underlying failure mode is unchanged, just concealed now.

The original car question is not ambiguous at all. And the specific responses to the car question weren't even concerned with ambiguity at all, the logic was borderline LLM psychosis in some examples like you'd see in GPT 3.5 but papered over by the well-spoken "intelligence" of a modern SOTA model.

> If this comes to pass OpenAI's value is near unlimited.

How?

If we have AGI, we have a scenario where human knowledge-based value creation as we know it is suddenly worthless. It's not a stretch to imagine that human labor-based value creation wouldn't be far behind. Altman himself has said that it would break capitalism.

This isn't a value proposition for a business, it's an end of value proposition for society. The only people who find real value in that are people who spend far too much time online doing things like arguing about Roko's Basilisk - which is just Pascal's Wager with GPUs - and people who are so wealthy that they've been disconnected with real-world consequences.

The only reason anyone sees value in this is because the second group of people think it'll serve their self-concept as the best and brightest humanity has ever had to offer. They're confusing ego with ability to create economic value.

"End of human-based value creation" is tantamount to post-scarcity. It "breaks" capitalism because it supposedly obviates the resource allocation problem that the free-market economy is the answer to. It's what Karl Marx actually pointed to as his utopian "fully realized communism". Most people would think of that as a pipe dream, but if you actually think it's viable, why wouldn't you want it?

These people don't mean for you to live in post-scarcity. If they did, they'd just alleviate at least some of the scarcity we have now.

At least in the US, they're pretty big on not doing that. Like explicitly.

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