dauhak

Their disclosed run rate was 14bn around the time of those filings IIRC, they started showing meaningful revenue around start of 2025, so if you just linearly extrapolate up that would give you ~7bn-ish actual revenue over that period. The more the growth is weighted towards the last few months the more that number goes down

So I don't think those numbers are really in tension at all

This makes no sense. You can describe the brain reductively enough and make it sound like it can't have an original insight either. Transformers are expressive enough function approximaters in theory, there's no reason why a future one couldn't have novel insights.

This is such a weird misconception I keep seeing - the fact that the loss function during training is minimising CE/maximizing prob of correct token doesn't mean that it can't do "real" thinking. If circuitry doing "real" thinking is the best solution found by SGD then it obviously will

I think this is valid criticism, but it's also unclear how much this is an "inherent" shortcoming vs the kind of thing that's pretty reasonable given we're really seeing the first generation of this new model paradigm.

Like, I'm as sceptical of just assuming "line goes up" extrapolation of performance as much as anyone, but assuming that current flaws are going to continue being flaws seems equally wrong-headed/overconfident. The past 5 years or so has been a constant trail of these predictions being wrong (remember when people thought artists would be safe cos clearly AI just can't do hands?). Now that everyone's woken up to this RL approach we're probably going to see very quickly over the next couple years how much these issues hold up

(Really like the problem though, seems like a great test)

Do you work in ML research on LLMs? I do, and I don't understand why people are so unbelievable confident they understand how AI and human brains work such that they can definitely tell what functions of the brain LLMs can also perform. Like, you seem to know more than leading neuroscientists, ML researchers, and philosophers, so maybe you should consider a career change. You should maybe also look into the field of mechanistic interpretability, where lots of research has been done on internal representations these models form - it turns out, to predict text really really well, building an internal model of the underlying distribution works really well

If you can rigorously state what "having a world model" consists of and what - exactly - about a transformer architecture precludes it from having one I'd be all ears. As would the academic community, it'd be a groundbreaking paper.

ToM is about being able to model the internal beliefs/desires etc of another person as being entirely distinct from yours. You're basically bringing up a particular implementation of long-term memory as a necessary component of it, which I've never once seen? If someone has severe memory issues, they could forget who Steve is every few minutes, but still be able to look at Steve doing something and model what Steve must want and believe given his actions

I don't think we have any strong evidence on whether LLMs have world-models one way or another - it feels like a bit of a fuzzy concept and I'm not sure what experiments you'd try here.

I disagree with your last point, I think those are functionally the same sentence

And people with short term memory loss nevertheless have theory of mind just fine. Nothing about LLM's dropping context over big enough windows implies they don't have theory of mind, it just shows they have limitations - just like humans even with "normal" memory will lose track over a huge context window.

Like there are plenty of shortcomings of LLMs but it feels like people are comparing them to some platonic ideal human when writing them off

If you have virtually no pricing power and have to drop your $200/mo to $15/mo that's a big deal if your $300bn valuation is implying that not happening, which is what OP's point is about

Idk what you mean by saying this doesn't preclude a monopoly - having your pricing power eroded by competition is kinda one of the key features of what a monopolistic market isn't

Executive branch has leeway to decide on what to fund within the parameters set for the program by Congress. It can evaluate grants and set processes but not completely change the acceptance criteria or scope, which is under the jurisdiction of Congress - USAID is jointly under the purview of the executive and legislative branches. This isn't a "team" thing - Congress sets the scope of what USAID should be doing, and anyone changing that - or dismantling the program altogether - without their authority is overreaching

And again, my main issue here is that under any reasonably interpretation, Musk would qualify as a Principal officer, which as the Appointments clause of the Constitution clearly lays out requires Senate approval. It is beyond ridiculous that the head of a new "Department" who seems to have unilateral power over other departments now, is not subject to any kind of oversight or accountability to other branches of government - this is exactly the kind of shit the checks and balances were designed for

Hey man, if you wanna make a point just make a point - no need to try the whole snarky rhetorical thing

Ofc not every decision is fully democratic, but the people making them are beholden to rules and systems which are - or at the least, have a clear chain of command back to individuals who Congress has direct authority over. No one ever said you needed 100% democratic oversight on every action, as long as those actions are obeying the system that was democratically established

The problem is doing it in an extra-legal way, where the Executive Office is giving a crony power his branch doesn't/shouldn't be able to bestow, where people telling this crony no when he tries things he shouldn't be able to do all seem to get put on leave etc

I'm very pro some systematic auditing/cleaning of out sclerotic waste, but I don't see how anyone can look at the way this is being handled and not be incredibly worried

I think it's the second-order stuff here. Even assuming Musk were to do a fantastic job at just clearing out inefficiency in a smart way (which seems unlikely given the actions he's taken/leaks around cutting funding based on key-word matching etc.), the higher-order point that someone can just buy their way into the President's inner-circle and have complete free-reign to seize government operations and make changes with 0 transparency/accountability seems like it does just stupid amounts of harm to the integrity of the system

I mean those same conditions already just lead the human to cutting corners and making stuff up themselves. You're describing the problem where bad incentives/conditions lead to sloppy work, that happens with or without AI

Catching errors/validating work is obviously a different process when they're coming from an AI vs a human, but I don't see how it's fundamentally that different here. If the outputs are heavily cited then that might go someway into being able to more easily catch and correct slip-ups

They're also still deep in their loss-making phase, the whole "incumbent squashing upstarts" stance is a lot easier to pull off when you're settled and printing money

Yeah that's a fair point! It's def a more general tech thing, but I think there are a couple specific reasons why it comes up more here though. Firstly, I think most tech does not improve at the insane rate that AI has been historically, so people's perception of capabilities become out of date just incredibly rapidly here (think about how long people we're banging on about "AI can't draw hands!" well after better models came out that could). If you think of the line as a way to say "don't anchor on what it can do today!" then it feels more appropriate to go on about this more for a more rapidly-changing field

Secondly, I think there's a tendency in AI for some ppl to look at failures of models and attribute it to some fundamental limitation of the approach, rather than something that future models will solve. So I think the line also gets used as short-hand for "Don't assume this limitation is inherent to the approach". I think in other areas of tech there's less of a tendency to try to write off entire areas because of present-day limitations, hence the line coming up more often

So you're right that the line is kind of universally applicable in tech, I guess I just think the kinds of bad arguments that warrant it as a rejoinder are more common around AI?

Yeah obviously motivations are murky and all over the place, no one's free of bias. I'm not taking a strong stance on whether they're right or not or how much of it is motivated reasoning, I just think at least quite a bit is genuine (I'm mainly basing this off researchers I know who have a track record of being very sober and "boring" rather than the flashy Altman types)

To your point, yeah the models still suck in some surprising ways, but again it's that thing of they're the worst they're ever going to be, and I think in particular on the reasoning issue a lot of people are quite excited that RL over CoT is looking really really promising for this.

I agree with your broader point though that I'm not sure how close we are and there's an awful lot of noise right now

> Is the key idea here that current AI development has figured out enough to brute force a path towards AGI?

My sense anecdotally from within the space is yes people are feeling like we most likely have a "straight shot" to AGI now. Progress has been insane over the last few years but there's been this lurking worry around signs that the pre-training scaling paradigm has diminishing returns.

What recent outputs like o1, o3, DeepSeek-R1 are showing is that that's fine, we now have a new paradigm around test-time compute. For various reasons people think this is going to be more scalable and not run into the kind of data issues you'd get with a pre-training paradigm.

You can definitely debate on whether that's true or not but this is the first time I've been really seeing people think we've cracked "it", and the rest is scaling, better training etc.