[thepasch]

Article title: “The US is winning the AI Race”

Article content: “The US are capitalizing on AI the best”

A lot of assumptions there that no one can actually verify as true right now. If commercialization into rent-seeking SaaS landscapes is the endgame, then yeah, the US is winning the AI race. If individualization, local LLMs, and consumer hardware are the endgame, China is winning the AI race. If it’s something entirely different - if LLMs are the wall and research is what grants the next breakthrough, or if compute and memory requirements take a dive, or whatever; then we have no idea who’s winning the race because that stuff is mostly happening behind closed doors.

[SubiculumCode]

That seems like a lot of rationalization to me. China is pursuing these because they cannot compete on the frontier. Yes, there is a possibility that all that compute is not needed, but it is a rather remote possibility, and there is no doubt that, given the choice, China would be pursuing frontier model building with closed, propietary-only offerings.

[nradov]

All that compute is not needed. We have an existence proof from biology in the form of natural intelligence that much greater efficiency is possible. However, achieving dramatic improvements in compute efficiency will depend on unpredictable scientific breakthroughs. Personally I suspect that an entirely new hardware architecture will be needed, although I don't have any hard evidence to back that up.

[logicchains]

>We have an existence proof from biology in the form of natural intelligence that much greater efficiency is possible.

It's only a proof that it's possible with 18+ years of training.

[nradov]

In certain ways my dog has more generalized intelligence than any LLM, and I trained her in only a few months with a modest investment in dog treats.

[ribosometronome]

>from biology ... much greater efficiency is possible

Those are much more specialized models with pretty mediocre tokens per second.

[pwndByDeath]

Perhaps tokens is a dead end?

[SpicyLemonZest]

Perhaps! But perhaps whatever human brains use instead of tokens is not as amenable to scaling or copying.

[Matl]

I dunno, DeepSeek v4 Pro is rather on par as far as I can tell, maybe not with 5.5 Pro in all areas quite yet, but close.

I think China is thinking more about the application layer on top of models as going to matter more than the models themselves, so they don't need to gatekeep the models as much.

[seanmcdirmid]

China is competing in value AI because they cannot work at the frontier, but how is this bad at all? It’s like how the USA has the best drones but they are a few million dollars apiece while China has DJI.

If China could work at the frontier, I don’t know, I kind of think they would still be dumping a lot of resources into exploring the value side since they have that culture already in place.

[SubiculumCode]

I did not imply it was bad. I implied that competing in value AI is the only option that China-based AI companies have due to limitations in compute.

[seanmcdirmid]

This is true, but I don't think they would all be rushing to frontier if that option was available. Chinese are used to working with constraints to their benefit, they would see the price of working at frontier and make hard choices that maybe we can ignore in the states.

[YetAnotherNick]

Well China is consistently 6 months behind the frontier labs(possibly because they can they harvest data from released frontier models). If the scaling continues, US will win, but if not then China will win as the models will converge.

[SubiculumCode]

The non-release of Mythos tell you the future of that, so long as they can keep the weights from being exfiltrated. Once models become true national security threats, they won't be released in their full form. The hitch-a-ride approach becomes less capable of keeping up.

[nradov]

How would they prevent distillation? That would seem pretty tough to block for any LLM available for commercial use.

[philipkglass]

This post claims that Opus 4.7 has introduced some detrimental changes to stymie distillation:

https://old.reddit.com/r/Anthropic/comments/1snorbg/the_bigg...

I don't know enough about distillation to understand how much this hinders/slows the process, but it sounds at least superficially plausible.

[SubiculumCode]

By only providing degraded models to use commercially outside national defense applications would be my guess. As soon as models are a threat in terms of enabling biowarfare etc, then they just are not going to be generally released.

Honestly, I think its quite possible that models will be retrained with gaps in their knowledge. e.g. a coding model for commercial use probably doesn't need to have deep knowledge of biology, and training on biological sciences probably doesn't help those evals much.

Honestly, I'd welcome such an approach.

[nradov]

What a hilariously uninformed comment. LLMs are not the limiting factor in biowarfare.

[YetAnotherNick]

We were talking about winning commercially, not on model quality.

[cyberge99]

Forgive me if this is a naive assumption, but wouldn’t large language models be fundamentally different for a language that is largely symbols? Again, my understanding of Mandarin is limited if it exists at all.

[doph]

All tokens are symbols. All of the frontier models speak Mandarin.

[boothby]

This is why misspellings and homophones are tells of human righting. LLMs strongly prefer word-level tokens, and word substitutions follow semantic similarity and not the more human auditory similarity.

[omneity]

Funny, I’ve been cracking[0] at this exact problem with a purpose-built model[1]:

0: https://huggingface.co/posts/omarkamali/593639295164067

1: https://omneitylabs.com/models/sawtone

[jddj]

Claude the other day wrote code where one of the bytes in the array was 0xO5.

That's zero ex oh (the letter) five

[mejutoco]

> righting.

> LLMs strongly prefer word-level tokens, and word substitutions follow semantic similarity and not the more human auditory similarity.

Is this an elaborate joke or your full-word misspelling of writing is both agreeing with your statement (word substitutions) and contradicting it (not semantic but only pronunciation similarity)

[calfuris]

I don't see the contradiction, unless you believe that the grandparent comment was written by an LLM.

[wat10000]

"飞机" and "airplane" aren't fundamentally different in terms of how they're represented to a computer. Especially for an LLM, where tokenization likely turns each of those into a single token.

[throwaway27448]

> China is pursuing these because they cannot compete on the frontier.

? Claude, ChatGPT, etc are heinously expensive for tiny benefits lmao. Local + efficient is clearly the future

[visarga]

> ? Claude, ChatGPT, etc are heinously expensive for tiny benefits lmao

Unfortunately local inference is inefficient, 100s of times more inefficient than cloud. When you answer one request at a time you still have to fetch all active weights into compute units, once every token. When you run a batch of 300, you load it once and compute 300 at a time.

Compared to cloud, local inference is less flexible. You can't scale up 5x or 20x, can't have spikes, and pay for it no matter if you use it or not. But usage factor is very low, like 5%. And to run a decent model your system costs $2000 or more.

[ToucanLoucan]

AI boosters cling to this notion because it's the only way the massive data center buildouts make any sense at all. I guess you could say the US is winning the frontier AI race. Okay. I'm never going to grant a cloud service access to all the contents of my hard drive, that's just never going to happen, so if you expect me and a lot of people like me who feel similarly to get on this train, you better have a local, lightweight model too or we're not even having a discussion, the answer is just no.

[Our_Benefactors]

The thing is, frontier model providers don’t take your feelings into account even a little bit. It’s totally irrelevant to the discussion about the service they can provide, because that service is predicated on access to high power GPU slices that local models can’t touch. Those providers won’t be in an existential crisis because some people choose the privacy route, it’s a cost of doing business.

[ToucanLoucan]

Right but that service being sold is predicated on products being sold to users, yes? Or are we still pretending that the hyperscalers can just pass the same $20 billion between themselves and that's going to be a growth industry forever?

[ElevenLathe]

I suppose its possible that all the value to pay back the datacenter construction can be squeezed out of enterprise contracts where your employer can assent on the privacy questions, probably with some kind of complicated contract and insurance regime regulating things.

Even if so, if China is coming behind 6 months later selling laptops with hyper-efficient local models that are 80% as good as "frontier" ones, I imagine they'll get the consumer business AND a fair share of the enterprise business as IT managers look at their options during the next refresh cycle.

Given economies of scale, I think it's ultimately inevitable that the enterprise more-or-less follows the consumer on this, and the consumer is going to prefer local models. There's no ongoing cost after the initial purchase, and your data at least nominally stays within your control.

[Our_Benefactors]

If we are betting on which is an easier sale, $20-100 a month w/tech support included vs $5k-10k and a requirement for moderate technical ability, I would invest in the former not the latter being the proposition that drives the conversation about AI use.

[ericmay]

> ? Claude, ChatGPT, etc are heinously expensive for tiny benefits lmao. Local + efficient is clearly the future

Corporate America is where the money is, and corporate America will dictate what products are successful by virtue of spend. Individuals aren't going to be paying $100s or $1000s/month en masse for these models but businesses will be. Being local and efficient isn't that important at this stage but even so as American companies continue to scale and invest they'll be able to make those models more local and efficient if the market wants it. Sort of like how you had a big, giant desktop computer and now you've got a super computer in your phone which is in your pocket. Going straight to "local and efficient" means going straight to being behind because at some point, perhaps now even, the local and efficient model won't be able to keep up.

For some reason people think that they somehow know something that Google or Nvidia or whoever, with hundreds of billions of dollars of real money at stake don't already know and it's both amusing and bizarre to see this play out again and again in off-hand comments like "lol tiny benefits".

You buy an iPhone even though the cheap-o Wal-Mart Android phone for $100 "does the same thing". Except that in this case the Android phone just puts you out of business while those spending big money for "tiny benefits" beat you in the market.

[ForHackernews]

> You buy an iPhone even though the cheap-o Wal-Mart Android phone for $100 "does the same thing".

People buy iPhones because of status signalling and network effects, neither of which appears to apply to AI model choice. LLMs are already rapidly on the way to being interchangeable commodities.

[ericmay]

No they don't, it's not 2008. Anybody off the street can get an iPhone or a free iPhone with a mobile plan. They're commodity products. Even homeless people have them.

To the extent LLMs are commodity products you're right (so far), but that is limited to the main model providers, such as ChatGPT, Claude, Gemini, &c. with interoperability on cloud platform providers and other technology providers like an Apple offering you a choice of LLM with Siri or something.

If you want to suggest that some other model is in the same bucket as those primary 3, it goes back to the crappy, cheap phone analogy which is accurate. Yea you can make calls with it, but you make calls better with an iPhone.

[ForHackernews]

Ironically you are participating in the social signalling ("crappy, cheap phone") phenomenon you claim doesn't exist.

https://mashable.com/article/apple-messages-green-doj

https://www.sfgate.com/tech/article/apple-green-bubble-messa...

[throwaway27448]

> free iPhone with a mobile plan

I get your point but in what sense is that "free"? What mobile plan giving you an iphone doesn't come with explicit debt?

[throwaway27448]

Corporate america is the past. Momentum is carrying capital out of the country. Pay attention to rate of change.

[ericmay]

What source are you using for your claim that capital is flowing out of the country? I'm curious to read more about it.

[throwaway27448]

I don't think that's a particularly bold claim after thirty straight years of moving supply chains overseas. Capital is, inherently, the means of production. The world where we could compete is gone.

[zozbot234]

"AI in the datacenter" and "AI on local consumer hardware" will eventually be two separate niches with entirely different capabilities, at least if scaling laws continue unchanged and there's no near-term inherent limit to AI smarts. The real point of the datacenter is to be able to do datacenter-scale things. But you don't need that kind of vast compute to run even the largest open models today: on prem hardware can do it easily especially if you're OK with a somewhat delayed response.

[m3kw9]

even without any of that anyone you ask who's used AI to any professional degree will agree US is winning AI race right now. Future, who knows