This is just speculation on my part, but LLMs work best when they get immediate, verifiable feedback on their task, and the kind of physical optimizations they mean might not give that to LLMs.
The right way is to throw LLMs at building tools that reframe the problem into a shape LLMs are good at navigating, and then have LLMs use those tools to solve it.
Yes, they are, but the most important subtasks of designing a CPU are not physics related. They are picking the right parameters for things like: how wide do I make this bus, how many registers do I put in the register file, how large do I make this cache, how deep do I make this pipeline, etc., etc. To find optimal parameters requires a lot of simulations, and humans do this, but LLMs could do them just as well and maybe better because they excel at tedious work.
How do you know this is not true with other vendors? I'm not defending them but I wouldn't believe anyone in this business unconditionally. Anthropic agent fwiw is not open source, gemini and codex are.
People have found many nasties embedded in Claude Code over the last couple of years. You can't trust a closed source harness. You can barely trust an open source one.
They do invent code solution for the problem that exists in your codebase. Latter implies that the code solution LLM synthesizes is usually unique of a kind, so, it's not a copy-paste neither it is a simple extract from "another codebase" and adopted.
IMO they operate pretty similarly to humans - we synthesize our solutions, and therefore build-up our knowledge, by collecting knowledge from multiple other sources, including technical books and blogs, open-source code repositories, and our past experiences.
My argument is if AI companies are ignoring copyright law and looking at all training data as commons, then we should look at LLM output as something that is not protected by copyright law.
Of course I'm a bit naive here, because we are talking about the richest companies in the world with lot of money to spend on lobbying (or bribes).
I wanted to understand your background first because what you initially said is a very oversimplified view of LLM mechanics, and generally not quite the way how software is in practice written. Since you didn't answer that question, I will assume that you're not a SWE by a call. To give you an example of what I am trying to convey is: imagine a data-intensive workload hitting your storage/database/kernel implementation, and it's painfully slow, your customers are not happy. Then you as engineer sit down, spend days profiling and understanding the code, researching about existing algorithmic solutions to the same or similar issues found in the wild, you read some open-source implementations of viable approaches, you ditch some, some you take, you also read books, articles, other peoples experiences etc. And finally you end up, let's put it bluntly, with some sharded data structure by which you solve the bottleneck. It's not novel, the technique is so common and is already implemented across many many different products in slightly different flavors so I am wondering why do you think this is not a copyright breach but the LLM, which does more or less the same thing, is?
Benchmarks will be interesting to see but I doubt the cores alone will be able to reach beyond ~500GB/s of BW, and even if they match that figure it will be great.
Not to digress from the core argument of Claude vs GLM being open weights….
I have both plans. Claude monthly €20 and Z’s €18 monthly. Running GLM-5.3 high on their monthly plan will hit quotas absurdly fast compared to Opus 5 High on Claude code. It’s almost unusable for AI driven development. I ended up using the Z plan for using GLM-5.3 as a detailed security reviewer and adversarial feedback. For that, it is much better than Opus which will flag and bail out for even simple security tasks that are aimed at defense.
But, it was enough for a customer like me who tried them at good faith to walk away and find their competitors..
I like the diversity of LLMs as of today and prefer to not tie myself to one big plan with any vendor. If they don’t prefer me as a customer, then I will accept that, and move away.
Just use GLM-5.3 Flash via OpenRouter. It's dirt cheap especially relative to how capable it is. While the Z.ai coding plan was a decent deal in the past I always ran into limiting with it and since I use it intermittently for personal projects my usage wasn't always enough to make the math work - the a la carte pricing via OpenRouter makes this a non-issue.
There's also a new free stealth model available that's more likely than not in the GLM family. This seems to happen every few months for a week or two and represents a good savings opportunity.
> Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale.
you can also derive some stats from the ~10T tokens a day on 100k devices, 100M / device / day, but then one has to account for the multi-gpu model size, and I need coffee before I go there
The mainstream consensus on PRC silicon has been "the gap exists, but is closing" for a long time[1][2]. I think you might be confusing the algorithmically-cast shadows on the cave wall that you're sat in front of for a reasonable and unbiased sample. Can you provide any reason to believe that your data points are actually generalizable? From your tone it sounds more like you're a victim of getting outrage baited into engagement, and mistaking observations made in that envelope for a high quality data source. You gotta watch out for that mate.
Third party here, I feel like I'm missing something. Your comment seems somewhat pointed and I don't understand what exactly you're asking for?
Weren't they just saying that it's self-evident that preventing a manufacturing superpower - one with a significant pool of industrial engineers and effectively unlimited money & government backing - from acquiring some good is a temporary measure? Because if they have sufficient incentive, they would just... Learn to build it themselves?
After all, their entire nation is built around building things, and catching up / leap-frogging is much easier than starting from scratch.
> in 2022, mainstream and westoid virtualism opinion was ``china is cut off from the future now`` (cue the clownface here). ``we cut off chinas legs`` yadayada
maybe where you were paying attention. were they also certain that americans wouldn't be paying the tariffs?
but lots of people who saw what china was doing with renewable energy and EVs rightly suggested this would put them on the path of independence w/r/t GPUs and LLMs.
this isn't like the ol' days where "japan can't do software". we knew china can.
I’ll just note that NVidia’s moat has never been inference, and there are many chips used at a much larger scale than Chinese chips for inference like TPUs and AMD chips.
The other part is that it’s a bit of a meme here to say that the chip restriction is actually helping China (or shall I say, coordinated effort?). For once, we know that China has put a lot of pressure on the US to relax these controls multiple times. In addition to large chip smuggling networks (e.g. 22% of NVidia’s worldwide revenue magically comes from Singapore, and the ratio has been growing).
Lastly, assuming acceleration in AI (which we ARE seeing), there might not be time to China to catch up. The best estimate right now is that the first EUV chips from China will not come out before 2030. By that time who knows how powerful AI will be.
All I’m saying is that the discussion is so one sided and a bit baselesss with no nuance, that it seems either a meme/groupthink in the community or coordinated. If anything, the data suggests that the US should increase its export controls and better track the tech supply chain if it wants to further curb Chinese progress.
When you say AI will be powerful, what do you mean? Like in terms of national power, will AI let the US fight and win a war against China despite China's size, manufacturing base and possession of nuclear weapons? Will AI let the US beat the Chinese in manufacturing costs and scale despite China's greater adoption of industrial robots and larger number of skilled workers? Or will AI just be really good at writing software?
Because if the AI is just really good at writing software, I am not sure why China has to "catch up". Seems to me China can just treat AI like other technologies. Let the US pay most of the R&D costs, then come after and treat the technology as a commodity. Sell it better and cheaper and at scale.
I am not sure why it's a big deal for China if China is a few months behind the US in terms of the very frontier AI. Just like I'm not sure if it's a big deal which country has the biggest super computer in the world.
If AI is super human, it can beat any nation with today’s technology. It can recursively self improve while suppressing any other lab (think about how good it is in hacking computer systems).
I am not inventing a winner take it all scenario, that is what driving the race.
But, like, how does that smarts lead to beating nations with today's technology? How would an AI stop an incoming nuclear ballistic missile? I've heard on podcasts with MIT professor Ted Postol that based in the laws of physics intercepting these is basically impossible, because of the speeds and physics involved. Can AI discover new physics to overcome those limitations?
If AI cannot stop nuclear ballistic missiles, then how does AI give the US power over China? Seems to me that after AI we have the same mutually assured destruction we have today. In which case America cannot really stop China from progressing or threaten China too much.
Seems to me being super human intelligent does not inherently guarantee power. Just like the smartest person in the world does not always win in a fist fight or a gun fight or war. I.e. Stephen Hawking might have been smart, but he did not have physical strength and power. Power and smarts are not always related.
I just gave you a scenario above - that it hacks every computer system in China, including those controlling nuclear missiles. But a super intelligent AI can invent scenarios that almost by definition we cannot think of.
I would assume that the computer systems in China that control nuclear missiles, as well as our computer systems that control nuclear missiles, are air gapped and therefore not hackable no matter how smart the AI is.
This only demonstrates imposed limits you are able to imagine, not what’s possible in all scenarios. A super intelligent AI can bribe, social engineer and do anything it wants. No system is foolproof.
Historically civilizations that have been only at a slight technological advantage have completely dominated those that are slightly behind.
And we already see AI’s super human ability in computer systems. It will be able to be Omni present and completely control every computer in any place, and bribe and social engineer and mimic any person digitally. All while doing in an instant what a team of humans would require years.
I’m really surprised by the lack of logical thinking in HN when it comes to China.
Why do you think the Chinese are not using their ai chips, which afaics are from huawei, to train their models? If that is true, which I think it is, we don't have to wait until 2030 to see if they're gonna match the performance of nvidia GPU. The evidence is already here - glm is among the most competitive models out there.
Look up reports by The Information, DeepSeek’s training of its big models are all done on NVidia’s chips (more than that - on smuggled Blackwell as well). There have been attempts by many Chinese labs to train on Huawei h chips, but they have all been limited to more minor models or distillations of the bigger ones.
Unless you have the source for the latter it's nothing more than propaganda. Also, DeepSeek report is 2-3 years old, and at that time there were no huawei chips, at least not known to the public. This is a different model, in different age where huawei chips are already delivered into the production as we see
The idea that China wasn’t working hard to build their own chips is a political talking point period. Nothing has been sped up here, China was always going to do this and the backlash you see from people who are against it is only proof that restrictions do hurt them
I do believe your first sentence, but the second one ... doesn't really follow.
The unnatural and sudden nature of the US chip embargo has galvanized the Chinese companies to speed up development. It makes economic sense to do so rather than pay inflated rates to essentially smuggle a necessary good, while never knowing if the embargo will be tightened and those under-the-table supply chains dry up as well.
Yes, there was a plan, but what was planned for 5 years from now is being done today, because if you wait 5 years, you'll be so far behind thanks to the effect of the embargo.
The AI revolution has led to everyone including US companies working to build their own GPUs there is zero proof or reason to think embargo’s are the cause. As I said, China was already working to build their own capacity
It's an implied corollary: when folks say that "blocking US Chips is what led China to build it's own" they are implying it is US export restrictions that pushed China to innovate. OP is saying this isn't necessarily true. I don't think it is either: China seems to have succeeded due to a centrally planned economy that values independence, with high amounts of strategic government led technology investment.
> All I’m saying is that the discussion is so one sided and a bit baselesss with no nuance, that it seems either a meme/groupthink in the community or coordinated. If anything, the data suggests that the US should increase its export controls and better track the tech supply chain if it wants to further curb Chinese progress.
Or, to put it bluntly, you are a proponent of the trade war.
Are there any places that require winter tires by law that don’t have cold winters? I don’t know of anywhere in the US that even requires winter tires, although I think some states require you to have winter tires OR chains.
Sometimes the winters may be cold, some other times they may be exceptionally warm, and sometimes they're a mix of both. The law remains the same regardless what the winter was or is like. Countries that have exceptionally warm winters do not really have a winter conditions so I'd guess their law wouldn't have a requirement for winter tires or chains
Yes of course, anyone who lives somewhere with winter knows that some winters are colder than others. In the late fall when it is time to swap the tires, you don’t know in advance whether the winter will be harsh or mild, but people put snow tires on anyway in places where harsh winters are common.
It would only make sense to require them every winter if the large majority of winters were quite cold. As such, nowhere in the US requires winter tires, but someone else noted that Quebec does. This makes sense, since even a mild Quebec winter will spend most of the time getting below freezing.
I would be very surprised if there were a place that required winter tires by law where the winter does not always get and stay at or below freezing for significant periods of time almost every winter.
There are mountain passes in Oregon with posted signs during winter that say "winter tires or chains required". I'm not sure if you can actually be cited by an officer for not having them, but certainly you are a hazard to everyone on the road if you don't have them :)
Didn’t know it was required, but it makes sense, as it is indeed quite cold. Good thing I always have winter tires on in the winter anyway, so I’m not breaking the law when I drive to Montreal!
I started using gemini with caution given the "much worse" benchmarking points it has gotten and still does but in practice there's very little evidence I found in comparison to claude models. It performs really well on non trivial tasks.
I wonder why not or you meant not suitable yet?
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