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I don't think GLM is really that high. In my experience it may be more comparable to, like, Xiaomi V2-Flash or Minimax M2.1. Chinese ecosystem is uneven, and GLM team has massive clout thanks to their Tsinghua ties. I believe they're a bit overhyped.
It probably will have the advantage, but a) unclear what this advantage gives you practically, and b) the divergence from compounding this advantage keeps getting postponed. Roughly a year ago, Dario Amodei wrote:
Well, American companies already have millions of chips. We're nearing 2026. Multiple models trained on those superclusters already got released, RL cost is now in high millions, probably tens if not hundreds of millions for Grok 4 and GPTs, and likely Claudes. Result: Opus is not really far smarter than V3.2, an enhanced version of a year-old model Dario writes about, with total post-training costs around $1M. On some hard math tasks, V3.2 Speciale is not just like 20x cheaper per task but straight up superior to American frontier at the time of release. The gap has, if anything, shrank. Wasn't «gold at IMO» considered a solid AGI target and a smoke alarm of incoming recursive self-improvement not so long ago? V3.2-Speciale gets that gold for pennies, but now we've moved goalposts to Django programming, playing Pokemon and managing a vending machine. Those are mode open-ended tasks but I really don't believe they are indexing general intelligence better.
Maybe we'll see the divergence finally materializing in 2026-2027. But I think we won't, because apparently the biggest bottleneck is still engineering talent, and Americans are currently unable to convert their compute advantage into a technological moat. They know the use cases and how to optimize for user needs, they don't really know how to burn $1B of GPU-hours to get a fundamentally stronger model. There's a lot of uncertainty about how to scale further. By the time they figure it out, China has millions of chips too.
There is an interesting possibility that we are exactly at this juncture, with maturation of data generation and synthetic RL environment pipelines on both sides. If so, we'll see US models get a commanding lead for the next several months, and then it would be ablated again by mid-late 2026.
V3.2 was a qualitative shift, a sign that the Chinese RL stack is now mature and probably more efficient, and nobody paid much attention to it. Miles is former Head of Policy Research and Senior Advisor for AGI Readiness at OpenAI, and he pays attention, but it flew under the radar.
Another reason I'm skeptical about compounding benefits of divergence is that it seems we're figuring out how to aggregate weak-ish (and cheap) model responses to get equal final performance. This has interesting implications for training. Consider that on SWE-rebench, V3.2 does as well as «frontier models» in pass@5 regime, and the cost here is without caching; they have caching at home so it's more like $0.1 per run and not $0.5. We see how even vastly weaker models can be harnessed for frontier results if you can provide enough inference. China prioritizes domestic inference chips for 2026. Fun fact, you don't need real HBM, you can make do with LPDDR hybrids.
But all of that is probably secondary to social fundamentals, the volume and kind of questions that are economical to ask, the nature of problems being solved.
I think all of this is stages of grief about the fact that the real king is physics and we have a reasonably good command of physics. Unless AGI unlocks something like rapid nanoassembly and billion-qubit quantum computers, it may simply not change the trajectory significantly. The condition of being a smaller and, as you put it, dopey society compromises "compute advantage". Great American AI will make better robots? Well, it'll likely train better policies in simulation. But China is clearly far ahead at producing robots and can accelerate to tens of millions in little time given their EV industrial base, gather more deployment data, iterate faster, while American startups are still grifting with their bullshit targets. Similar logic applies in nearly every physical domain. Ultimately you need to actually make things. Automated propaganda is… probably not the best idea, American society is too propagandized as is. Cyberwarfare… will American AGI God really be good enough to hack Huawei clusters after their inferior Temu AGI has hunted for vulnerabilities in an airgapped regime for a few months? I think cyberwarfare is largely going dodo in this world, everyone will have an asymmetric defense advantage.
Obviously, that's still the most credible scheme to achieve American hegemony, conquer the light cone etc. etc. I posit that even it is not credible enough and has low EV, because it's an all-or-nothing logic where «all» is getting elusive.
Maybe it can't hack the servers directly if they're airgapped (though I wouldn't underestimate the power of some social-engineered fool bringing in a compromised USB) but it could hack everything around the servers, the power production, logistics, financing, communications, transport, construction. I doubt the servers even are airgapped, modern data centers are saturated with wireless signals from Wi-Fi peripherals, IoT sensors, and private LTE/5G networks. The modern economy is a giant mess of countless digital parts.
I think people underestimate the power of 'nation of geniuses in a datacentre', even without any major breakthroughs in physics, I think mere peak human-level AIs at scale could wipe the floor with any technological power without firing a shot. In cyber there is no perfect defence, only layers of security and balancing risk mitigation v cost. The cost of defending against a nation of geniuses would be staggering, you'd need your own nation of geniuses. Maybe they could find some zero-day exploits. Maybe they could circumnavigate the data centre and put vulnerabilities in the algorithms directly, find and infiltrate the Chinese version of Crowdstrike? Or just raze the Chinese economy wholesale. All those QR code payments and smart city infrastructure can be vulnerabilities as well as strengths.
China's already been kind of doing this 'exploit large high IQ population' with their own massive economic cyberwarfare program. It works, it's a smart idea. 10,000 hackers can steal lots of secrets, could 10 million wreck a whole country's digital infrastructure? You may have read that short story by Ci Xin Liu about the rogue AI program that just goes around causing human misery to everyone via hacking.
I believe that the physical domain is trumped by the virtual. Even nuclear command and control can potentially be compromised by strong AIs, I bet that wherever there is a complex system, there will be vulnerabilities that humans haven't judged cost-efficient to defend against.
I think it's funny that we've both kinda swapped positions on AI geopolitics over time, you used to be blackpilled about US hegemony until Deepseek came along... Nevertheless I don't fully disagree and predicting the future is very hard, I could well be wrong and you right or both of us wrong.
Eh, I think Pokemon and vending machines are good tasks. It's long-form tasks that matter most, weaving all those beautiful pearls (maths ability or physics knowledge) into a necklace. We have plenty of pearls, we need them bound together. And I don't think 3.2 does as well as Claude Code, at least not if we go by the 'each 5% is harder than the 5%' idea in these benchmarks.
You misunderstood my point. I am saying that hacking as such will become ineffectual in a matter of years. Automated SWEs make defense drastically advantaged over offense due to information asymmetry in favor of the defender and rapid divergence in codebases. This “superhacker AGI” thing is just lazy thinking. How long do you think it takes, between open source AIs that win IOI&IMO Gold for pennies, and formally verified kernels for everything, in a security-obsessed nation that has dominated image recognition research just because it wanted better surveillance?
A very American belief, to be sure.
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