My first impression of o3 (as available via Chatbot Arena) is that when I'm showing it my AI scaling analysis comments (such as this [LW(p) · GW(p)] and this [LW(p) · GW(p)]), it responds with confident unhinged speculation teeming with hallucinations, compared to the other recent models that usually respond with bland rephrasings that get almost everything correctly with a few minor hallucinations or reasonable misconceptions carrying over from their outdated knowledge.

Don't know yet if it's specific to speculative/forecasting discussions, but it doesn't look good (for faithfulness of arguments) when combined with good performance on benchmarks. Possibly stream of consciousness style data is useful to write down within long reasoning traces and can add up to normality for questions with a short final answer, but results in spurious details within confabulated summarized arguments for that answer (outside the hidden reasoning trace) that aren't measured by hallucination benchmarks and so allowed to get worse. Though in the o3 System Card hallucination rate also significantly increased compared to o1 (Section 3.3).

A relevant, very recent opinion piece that has been syndicated around the country, explaining the universal value of faith:
https://www.latimes.com/opinion/story/2025-03-29/it-is-not-faith-that-divides-us 

In a just world, mitigations against AI-enabled coups will be similar to mitigations against AI takeover risk.

In a cynical world, mitigations against AI-enabled coups involve installing your own allies to supervise (or lead) AI labs, and taking actions against humans you dislike. Leaders mitigating the risk may simply make sure that if it does happen, it's someone on their side. Leaders who believe in the risk may even accelerate the US-China AI race faster.

Note: I don't really endorse the "cynical world," I'm just writing it as food for thought :)

Is the central point here that a given input will activate it's representation in both the size 1000 and size 50 sub-dictionaries, meaning the reconstruction will be 2x too big?

i agree that there doesn't seem to be any sort of rigorous way to get off the crazy train in some principled manner, and that fundamentally it does come down to vibes. but that only makes it worse if people are uncritical/uncurious/uncaring/unrigorous about how said vibes are generated. like, i see angst about it in the ea sphere about the inconsistency/intransitivity, and various attempts to discuss or tackle it, and this seems useful to me even though it's still mostly groping around in the dark. in academia there seems to be a missing mood.

Rule and Example

Rules can generate examples. For instance: DALLE-3 is a rule according to which different examples (images) are generated.

From examples, rules can be inferred. For example: with a sufficient dataset of images and their names, a DALLE-3 model can be trained on it.

In computer science, there is a concept called Kolmogorov complexity of data. It is (roughly) defined as the length of the shortest program capable of producing that data.

Some data are simple and can be compressed easily; some are complex and harder to compress. In a sense, the task of machine learning is to find a program of a given size that serves as a "compression" of the dataset.

In the real world, although knowing the underlying rule is often very useful, sometimes it is more practical to use a giant look-up table (GLUT) [LW · GW] of examples. Sometimes you need to memorize the material instead of trying to "understand" it.

Sometimes there are examples that are more complex than the rule that generated them. For example, in the interval [0;1] (which is quite easy to describe, the rule being: all numbers are not greater than 1 and not less than 0), there exists a number containing all the works of Shakespeare (which definitely cannot be compressed to a description comparable to that of the interval [0;1]). 

Or, сonsider the program that outputs every natural number from 1 to ${10}^{{10}^{20}}$ (which is very short, because the Kolmogorov complexity of ${10}^{{10}^{20}}$ is low) will at some point produce a binary encoding of LOTR. In that case, the complexity lies in the starting index, the map for finding the needle in the haystack is as valuable (and as complex) as the needle itself.

Properties follow from rules. It is not necessary to know about every example of a rule in order to have some information about all of them. Moreover, all examples together can have less information (or Kolmogorov complexity) than sum of individual Kolmogorov complexities (as in example above).

After thinking about it more, it's possible your model of why Commitment Races resolve fairly, is more correct than my model of why Commitment Races resolve fairly, although I'm less certain they do resolve fairly.

My model's flaw

My model is that acausal influence does not happen until one side deliberately simulates the other and sees their commitment. Therefore, it is advantageous for both sides to commit up to but not exceeding some Schelling point of fairness, before simulating the other, so that the first acasual message will maximize their payoff without triggering a mutual disaster.

I think one possibly fatal flaw of my model is that it doesn't explain why one side shouldn't add the exception "but if the other side became a rock with an ultimatum, I'll still yield to them, conditional on the fact they became a rock with an ultimatum before realizing I will add this exception (by simulating me or receiving acausal influence from me)."

According to my model, adding this exception improves ones encounters with rocks with ultimatums by yielding to them, and does not increase the rate of encountering rocks with ultimatums (at least in the first round of acausal negotation, which may be the only round), since the exception explicitly rules out yielding to agents affected by whether you make exception.

This means that in my model, becoming a rock with an ultimatum may still be the winning strategy, conditional on the fact the agent becoming a rock with an ultimatum doesn't know it is the winning strategy, and the Commitment Race problem may reemerge.

Your model

My guess of your model, is that acausal influence is happening a lot, such that refusing in the ultimatum game can successfully punish the prior decision to be unfair (i.e. reduce the frequency of prior decisions to be unfair).

In order for your refusal to influence their frequency of being unfair, your refusal has to have some kind of acausal influence on them, even if they are relatively simpler minds than you (and can't simulate you).

At first, this seemed impossible to me, but after thinking about it more, maybe even if you are a more complex mind than the other player, your decision-making may be made out of simpler algorithms, some of which they can imagine and be influenced by.

OpenAI's o3 and o4-mini models are likely to become accessible for $20000 per month

Already available for $20/month.

The $20,000/month claims seems to originate from that atrocious The Information article, which threw together a bunch of unrelated sentences at the end to create the (false) impression that o3 and o4-mini are innovator-agents which will become available for $20,000/month this week. In actuality, the sentences "OpenAI believes it can charge $20,000 per month for doctorate-level AI", "new AI aims to resemble inventors", and "OpenAI is preparing to launch [o3 and o4-mini] this week" are separately true, but have nothing to do with each other.

I think it seems like a fine possibility in principle, actually; sorry to have given the wrong impression! It's not my central hope, since strategy-stealing seems like it should make many human-augmentations "available" to AI systems as well. This is notably not true for things involving, e.g., BCIs or superbabies.

That article is sloppily written enough to say "Early testers report that the AI [i.e. o3 and/or o4-mini] can generate original research ideas in fields like nuclear fusion, drug discovery, and materials science; tasks usually reserved for PhD-level experts" linking, as a citation, to OpenAI's January release announcement of o3-mini.

TechCrunch attributes the rumor to a paywalled article in The Information (and attributes the price to specialized agents, not o3 or o4-mini themselves).