> It's not clear if native multi-modal models have been tested. I would expect 4o/Gemini models to do fairly well on these visual tests

They have been and I do not expect them too. You can see my comment history talking about LLM failure cases.

I'd advise being careful about just trying to reason your way through things when you don't have significant experience in a domain. Non expert reasoning can lead to good guesses but should never also be taken with high confidence. It's important to remember that nuance is often critical in these issues and not accounting for them often leads to approximations giving you the opposite answer rather than a close enough one.

But as Francois points out, LLMs are compression machines. That's what the mathematically are. They are not reasoning machines. A lot of people don't want to hear this because they think it undermines LLMs and any criticism is equivalent to saying they're useless. But I still think they're quite impressive. Criticism is important though, if we are to improve systems. So don't get blinded by success.

> So, what do you think will happen if the above scenario plays out?

In the next 5-10 years I'm far more worried about people confusing knowledge and reasoning. It's not a thing most people have needed to differentiate in the past because the two are generally associated with one another. But LLMs are more like if Google could talk to you than when a parrot talks to you. If this sounds the least bit odd, I encourage you to dig more into these topics. They are not easy topics because they are filled with nuance that is incredibly easy to miss. I keep stressing this point but it was one of my big fears and people's egos often sets us back, especially when we have no trust in experts. It's crazy to think we know more than people who spend their lives on specific subjects and think intelligence in one domain translates directly to another. So not knowing (most) things shouldn't ever be taken as a bad thing. There's not enough time to learn everything. There's not enough time to learn most things. So focus on a limited set and for the rest maybe just to the point where you can see the level of complexity ahead. If things seem simple, you probably don't understand it enough. Remember, there's thousand page reference manuals on things as narrow as bolts because the details matter so much.

As to the problems you mentioned, I'm not sure how those would be solved with ML or even AGI. Technology can't solve everything and a lot of these issues have significant amounts of politics and social choice associated with them that results in many of the problems (including where nuance dominates in some things and then cascades because we're talking about complex topics at a very high level and our knowledge is gained through a game of telephone rather than academically or experientially).

I think we're more than 50 years out from post scarcity, which is to say that no reasonable prediction can be made. But is still up to us if we want to increase the odds. I also agree with Francis that OpenAI has set us back on the path to AGI.

As for the fear, it's natural. Fear does help us. It's a great motivator. But it too can cripple us, and when it does it can give life to the very thing we fear. So care is needed when analyzing. The problem isn't about people not thinking. Everyone does and everyone is doing it constantly, even our dumbest of friends and acquaintances. The problem is that people are not thinking deep enough and having high confidence when stopping early. I'm not telling you to not have opinions on anything, it's only natural to have opinions on most things. But rather to be careful with the confidence you attribute to those opinions and of others. Here's the thing, if you do gain expertise in any singular field, you'll see that there is this rich but complex landscape. There's a lot of beauty in the landscape but often many pitfalls that cannot be avoided without some expertise and many which are common to these entering a field. These are things not to get discouraged by but to be aware of and why formal educations are typically beneficial. It's also to note that there is great beauty in this chaos ahead, even if it can be hard to see through the initial part of the journey.