awongh

I think on the timescale of 10 years, that's a super likely scenario. But will it be any sooner?

For instance a Chinese EUV machine seems like it's very far away. Even if they have (steal/borrow) the necessary IP.

How long is the long run though? China doesn't have the chips, and probably won't have them for a while.

I agree in principle, but it could be more than 5 years, maybe 10. Who knows what things will look like then.

In a recent Dwarkesh podcast Dylan Patel breaks down how little compute the chinese labs actually have- not even the fact that they don't have access to new Nvidia chips and they're stealing them through shell companies- just that, even if they have cheap electricity, the compute just doesn't compare. Maybe even two orders of magnitude less. They couldn't get it even if they had the money. And if you look at how much more efficient newer chips are, that cuts the effective compute in half again. The conclusion was that they are at least 2-3 years behind.

For frontier labs the current compute seems to be driving model progress (in training) at least to some degree, even without true RSI, and this seems like it'll continue to keep any chinese model from drawing even with the frontier labs, at least for the foreseeable future.

Inevitably the chinese government will drive more funding in chip fab technology and the money will come around to build chinese data centers, but who knows how far off that is. A few different things in the tech tree need to fall into place. It doesn't seem like it'll be next year.

One of the optimistic aspects of what's happening is that it doesn't seem like the leading model capabilities are outpacing the more open ones by that much- that just as a general principal there are pretty ok models relatively close behind the best ones.

This could mean the long term viability of my open claw bot to shop for me- that openai / google etc won't control AI, and I'll still just be able to use a model via API to do what's actually in my own self-interest.

What's crazy is that the ToS is basically, "allow us, Amazon, to give you algorithmically higher prices- and if you try to circumvent that, you can't buy from us anymore".

I think the arguments for these kinds of near-monopoly companies being true market-driven capitalism seem more and more false.

The idea that Amazon will tell you that you don't even own the right to your own shopping experience tells you everything you need to know. Apple owns your device and Amazon owns the right to show you whatever price/product they want, regarless of your search intent.

I didn't realize how much money Uber makes from ads.... Why does every business devolve into an ad platform?

I get the structural comparison they are trying to make.

But mortgages are not a frontier AI lab.

They try to draw a comparison to the valuation of the real estate and the valuation of the hyper scalers in the markets.

I would argue that the demand and valuation of a house is less elastic than AI. While a house’s value may continue to appreciate in the market there is an upper bound for the price of a house set by people’s income. We don’t know yet what the value of AI is. The underlying product, the model keeps improving and therefore increases its value. A house is still fundamentally a house a year later and doesn’t intrinsically appreciate in value.

From gpt-3 to gpt-5.5 there’s been a massive change in the underlying value of the product and company in a way that simply doesn’t happen with a house. That’s where the analogy breaks down.

The other thing with the AI debate is that it's nothing new- these kinds of failures are the same kind that have been going on for a long time, for the exact same reasons.

Any time a local government fails people these same kinds of incentive misalignments are at the real heart of it.

The fact that it has the word AI in it just gives it that cool cyberpunk dystopian sheen, where you call 911 and the AI says something about mechahitler instead of trying to help you.

If they are using a true agentic TTS / LLM setup then this is just a first pass for them to implement fully AI answered 911 calls.

Otherwise I can code this state machine condition for you in 10 minutes.

For the people who believe this, I think it deeply reflects how they think about all knowledge and the kinds of things they would use this technology for- "finding the answers to things without having to think about them"- that's only one way to use it, and the least compelling way, in my opinion.

I don't agree with the luddite-esque AI views like using ChatGPT makes you dumber and it thinks for you, but- this is a real case of why people are angry and why they should be concerned about deployment of AI.

The article says it's basically to handle high call volumes maybe during some kind of widespread incident. But in those cases why would AI even be needed- if calls are flooding in for something and people need to be put on hold why do you need AI to ask them if they are calling about incident x? Just automate a voice for that and ask them that while they're on hold / when the system first picks up.

This smells like a bandaid over an underfunded system, and a way to sneak in further cost-cutting in the future where the real calls will be actually answered by AI.

Most people mean this to say that 1 trillion is a lot of money, but it still comes back to what you believe AI is- in hindsight, does 1 trillion dollars to build the internet sound like a lot or a little? (That is, spending 1 year of USA's defense budget to get the entire internet)

It comes back to your perception of what AI is because to people who say AI is glorified auto-complete won't believe that the money is worth it.

The AGI-pilled true believers who say it will end all money and result in a post-scarcity world believe literally any amount is justifiable.

Most people, me included, land somewhere in the middle- it seems like AI is a humanity-level sea change in technology and how computers work and serve us. It seems plausible that a few trillion is a reasonable amount.

I don't want to save money so badly that I'd possibly undercut the quality of the code that gets created.

*edit to add: that code quality (or lack of quality) is it's own cost

what's the threshold for model routing where you're willing to trust the router?

For coding my own work I don't trust the model router, and it would have to be shown to be to save a real dollar amount.

From a buying perspective it's a hard sell to save x but lose out on bugs you are probably introducing at an unquantifiable severity and frequency. How much is it worth to hedge your bets by doing every single inference request on the frontier model?

How much will it cost to go back later and fix things, but also the meta question of how to be able to decide on a hypothetical unknowable? (You'll never know how much better or worse your code was gonna be, it's untestable at a project level)

I’m not sure I understand the analogy. You could say that the reason why artificial flavors work in food is because we know how to recreate flavors from first principle chemistry. The dislike of the artificialness of cheetos also doesn’t stop many many people from eating them.

I would say human made slop writing isn’t any better or worse quality than LLM writing.

Knowing if it was made by a real person makes a difference to me, but I also know it won’t to everyone.

> LLMs are really only capable of combining different things into output

In terms of how writers think about creativity this doesn’t reflect reality- there’s the old writer’s saying that there are only 7 stories (and other variations of this idea) and that writing is about creatively remixing these well-worn ideas.

The LLM’s capability to write acceptable fiction and nonfiction is coming soon if it’s not already here. (I think right now it still needs some high level input from humans, depending on length and topic)

It’s clear to me that, especially as LLMs get better and better, there’s only one real difference- it’s that I don’t want to hear what an LLM has to say. I’m only interested in what other humans have to say. I feel the same way about AI music- even if it sounds ok, even as good as the kind of unoriginal pop music that’s not AI made (but is a kind of it’s own slop) or a bad committee made hollywood movie, it’s still fundamentally more interesting than something that’s been generated. Even if it’s 100% fiction it’s still based on a real human’s life experiences.

It's crazy how much bigger OpenAI is than Anthropic.

Maybe they are hoping that when the bottom drops out they will just be able to buy Anthropic or OpenAI for a few tens of billion.

But maybe the TPU advantage is in inference? That's what I assume because the number of compute cycles are going to be all in inference vs training. So they could train on GPUs if they want.

It says the original report was in the Information, which I can't see, but I'm skeptical that they includes the training cost? And how much that changes the figure?

Does it have "massive" margins? Afaik no one has said publicly what margins there are on an API call?

It seems like there are some credible rumors that Google is actually winning in terms of actually building models that work and don't lose money- between how they're able to price them, the TPU advantage and their capex advantage (being able to raise debt + just having a lot of cash - well I said not lose money... more like not go bankrupt).

From the outside they look like they're behind in terms of frontier models, but I think they might be the best positioned to not go out of business when the bubble pops.

Also look at the fact that they've been able to deploy AI-assisted search at google scale. It must be another order of magnitude larger (at least) than the model deployments for OpenAI and Anthropic.

Of course unless you're inside Google it's impossible to know for sure.

I meant the attack would be the other way around- if an infected package had the git.exe file in their root.

Or, the infected package could also copy that file into the parent project's root.

I guess this is only specific to a file in the root of the repo, so it doesn't allow for an NPM supply chain attack?

It's interesting that all models seems to be unbiased out of the box so far- that is, mostly reflecting the training data (the internet).

The whole mecha-hitler thing doesn't seem to reflect fine-tuning, it was just a prompt change.

There's been some studies that suggest that certain usage of LLMs reduces political bias, which seems reasonable. Like, how credible is climate change, are Haitians eating pets, etc. THings that have a basis in fact.

I don't put it past Elon to train a model with political bias, just that it hasn't happened yet.

> But your brain works the other way around. First there is a concept, a feeling, an image, and then the words come out to describe it.

After this I can't take the essay seriously- this sort of blanket statement about the one true hierarchy of consciousness and knowledge is BS.

While it seems to have been disproven that words in other languages cause people to speak and think differently, that also doesn't mean that words don't have any effect on the way we think, or that the concept of a word always has to come before the word itself.

The reason why we experience the uncanny valley of LLMs is because they don't represent a true consciousness, BUT it's also clear that the architecture represents certain qualities of consciousness- as the models have scaled we can see that it has some other non-word related understanding.

The evidence points to consciousness as a set of interlocking systems- attention, long term memory, short term memory, emotions, etc.

For a lot of Americans the credit card system is another tax on being poor:

People with stable jobs and good credit qualify for no-fee credit cards with rewards / cashback. As a consumer you benefit financially from having a credit card. Those elsewhere in the thread worried about "debt" - you just set to auto-withdrawl the entire balance of the card every month from your bank account. Now you have free money. I can't think of a reason not to take advantage of this system in some way.

But people with unstable jobs and poor credit help subsidize these "higher-end" credit cards when they pay high interest rates on their because they missed payments or hold a balance over multiple months. For those people credit cards could help with monthly cashflow issues but are essentially a scam and not much better than payday loans.

Yet another system that American consumers are kind of forced to participate in that's a sort of tragedy of the commons (high-reward cards wouldn't exist without the exploitation of other people not savvy enough to avoid high interest and fees)

It’s all about perspective, because Vercel is probably another 3-10x markup on AWS?

These kinds of choices are kind of pricing range as engineering decisions in the end.

afaik prices for the big cloud providers haven't changed this much. Their instance prices are already inflated and they're probably willing to eat into their own margins to keep prices stable and just wait out the capex increase (also probably a drop in the bucket next to the hyperscale rollout).

People love to say how great it is for these alt clouds to have lower prices, until they're exposed to market forces with a company unable or unwilling to eat their profit margins.

As a teacher I understand how difficult it is to explain complex topics in a simple step by step way.

The site has some really impressive technical aspects, but the educational angle is the most rare and special! The simplicity of the language and explanations disguise how difficult this is to do.

This is the original use of the internet- giving away free knowledge to people, perfectly suited for the medium of a website.