Isn't the most likely explanation here that they needed to show in their earnings call how their bet on becoming AI infrastructure is leading to high revenue growth expectations, and that isn't happening (yet)?
The stock is currently at -17% in after hours trading.
So you need to do something that's good for your margins to show investors.
I mostly agree with you, I think what you're implying is correct on average, but I'm probably not the only one to whom HN is more addictive than Instagram, Tiktok and all the other classic social media apps.
They get boring much more quickly and also make me feel guilty about spending time on something so shallow, so it's very self limiting.
Do we know if this is another post training fine tune or based on a much larger new pretraining run (which I believe they were calling 'Spud' internally)?
This is correct in the sense that, if you were to build a zero emissions energy system from scratch with today's technology, your conclusion would be that you'd eventually have to do this.
But in much of the world, setting up PV is economically sound simply because it displaces a certain amount of kWh generated over the course of a year from other sources that are more polluting and more expensive.
In this regime, the dynamics of production over time don't matter yet.
At some point, when renewable generation has very high penetration, you'll reach a point where building more is uneconomical, and to then displace the remaining other power sources you'll need to overpay (ignoring externalities).
However, that's assuming no technological change on the way there, which is a whole separate topic.
Yeah, and if you ask it to be critical specifically to get a different perspective or just to avoid this bias, it'll go over the top in the opposite direction.
This is imo currently the top chatbot failure mode. The insidious thing is that it often feels good to read these things. Factual accuracy by contrast has gotten very good.
I think there's a deeper philosophical dimension to this though, in that it relates to alignment.
There are situations where in the grand scheme of things the right thing to do would be for the chatbot to push back hard, be harsh and dismissive. But is it the really aligned with the human then? Which human?
The capabilities of AI are determined by the cost function it's trained on.
That's a self-evident thing to say, but it's worth repeating, because there's this odd implicit notion sometimes that you train on some cost function, and then, poof, "intelligence", as if that was a mysterious other thing. Really, intelligence is minimizing a complex cost function. The leadership of the big AI companies sometimes imply something else when they talk of "generalization". But there is no mechanism to generate a model with capabilities beyond what is useful to minimize a specific cost function.
You can view the progress of AI as progress in coming up with smarter cost functions: Cleaner, larger datasets, pretraining, RLHF, RLVR.
Notably, exciting early progress in AI came in places where simple cost functions generate rich behavior (Chess, Go).
The recent impressive advances in AI are similar. Mathematics and coding are extremely structured, and properties of a coding or maths result can be verified using automatic techniques. You can set up a RLVR "game" for maths and coding. It thus seems very likely to me that this is where the big advances are going to come from in the short term.
However, it does not follow that maths ability on par with expert mathematicians will lead to superiority over human cognitive ability broadly. A lot of what humans do has social rewards which are not verifiable, or includes genuine Knightian uncertainty where a reward function can not be built without actually operating independently in the world.
To be clear, none of the above is supposed to talk down past or future progress in AI; I'm just trying to be more nuanced about where I believe progress can be fast and where it's bound to be slower.
> But there is no mechanism to generate a model with capabilities beyond what is useful to minimize a specific cost function.
Can you give some examples?
It is not trivial that not everything can be written as an optimization problem.
Even at the time advanced generalizations such as complex numbers can be said to optimize something, e.g. the number of mathematical symbols you need to do certain proofs, etc.
I think you're misreading me. My point isn't that you can't in principle state the optimization problem, but that it's much easier in some domains than in others, that this tracks with how AI has been progressing, and that progress in one area doesn't automatically mean progress in another, because current AI cost functions are less general than the cost functions that humans are working with in the world.
The vibe coding maximalist position can be stated in information theory terms: That there exists a decoder that can decode the space of useful programs from a much smaller prompt space.
The compression ratio is the vibe coding gain.
I think that way of phrasing it makes it easier to think about boundaries of vibe coding.
"A class that represents (A) concept, using the (B) data structure and (C) algorithms for methods (D), in programming language (E)."
That's decodeable, at least to a narrow enough distribution.
"A commercially successful team communication app built around the concept of channels, like in IRC."
Without already knowing Slack, that's not decodable.
Thinking about what is missing is very helpful. Obviously, the business strategic positioning, non technical stakeholder inputs, UX design.
But I think it goes beyond that: In sufficiently complex apps, even purely technical "software engineering" decisions are to some degree learnt from experiment.
This also makes it more clear how to use AI coding effectively:
* Prompt in increments of components that can be encoded in a short prompt.
* If possible, add pre-existing information to the prompt (documentation, prior attempts at implementation).
"Informally, from the point of view of algorithmic information theory, the information content of a string is equivalent to the length of the most-compressed possible self-contained representation of that string. A self-contained representation is essentially a program—in some fixed but otherwise irrelevant universal programming language—that, when run, outputs the original string."
Where it gets tricky is the "self-contained" bit. It's only true with the model weights as a code book, e.g. to allow the LLM to "know about" Slack.
> That there exists a decoder that can decode the space of useful programs from a much smaller prompt space.
I love this. I've been circling this idea for a while and you put into words what I've struggled to describe.
> "A commercially successful team communication app built around the concept of channels, like in IRC."
> Without already knowing Slack, that's not decodable.
I would like to suggest that implicit shared context matters here. Or rather, humans tend to assume more shared context than LLM's actually have, and that misleads us when it comes assessing the aforementioned decoder.
But I think it also suggests that there is a system that could be built with strong constraints and saliency that could really explode the compression ratio of vibe coding.
It's not necessarily just the terseness. Terseness might be a selling point for people who have already invested in training themselves to be fluent with programming languages and the associated ecosystem of tooling.
But there is an entire cohort of people who can think about specifying systems but lack the training to sdo so so using the current methods and see a lower barrier to entry in the natural language.
That doesn't mean the LLM is going to think on your behalf (although there is also a little bit of that involved and that's where stuff gets confusing) but it surely provides a completely different interface for turning your ideas into working machinery
"[T]here is an entire cohort of people who can think about specifying systems but lack the training to sdo so so using the current methods and see a lower barrier to entry in the natural language."
"Specifying" is the load-bearing term there. They are describing what they want to some degree, how how specifically?
> But there is an entire cohort of people who can think about specifying systems but lack the training to sdo so so using the current methods
Nah, it will be extremely surprising if even 1 such a person exists.
On the other hand, there are lots of people that can write code, but still can't specify a system. In fact, if you keep increasing the size of the system, you will eventually fit every single programmer in that category.
Could you say more on how the tasks where it works vs. doesn't work differ? Just the fact that it's both small and greenfield in the one case and presumably neither in the other?
The stock is currently at -17% in after hours trading.
So you need to do something that's good for your margins to show investors.
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