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late version of a small language model
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Here some people have expressed interest in my take on AI broadly, and then there's Deepseek-Coder release, but I've been very busy and the field is moving so very fast again, it felt like a thankless job to do what Zvi does and without his doomer agenda too (seeing the frenetic feed on Twitter, one can be forgiven for just losing the will; and, well, I suppose Twitter explains a lot about our condition in general). At times I envy Iconochasm who tapped out. Also, this is a very niche technical discussion and folks here prefer policy.
But, in short: open source AI, in its most significant aspects, which I deem to be code generation and general verifiable reasoning (you can bootstrap most everything else from it), is now propped up by a single Chinese hedge fund (created in the spirit of Renaissance Capital) which supports a small, ignored (except by scientists and a few crackpots on Twitter) research division staffed with some nonames, who are quietly churning out extraordinarily good models with the explicit aim of creating AGI in the open. These models happen to be (relatively) innocent of benchmark-gaming, but somewhat aligned to Chinese values. The modus operandi of DeepSeek is starkly different from that of either other Chinese or Western competitors. In effect this is the only known group both meaningfully pursuing frontier capabilities and actively teaching others how to do so. I think this is interesting and a modest cause for optimism. I am also somewhat reluctant to write about this publicly because there exist lovers of Freedom here, and it would be quite a shame if my writing contributed to targeted sanctions and even more disempowerment of the small man by the state machinery in the final accounting.
But the cat's probably out of the bag. The first progress prize of AI Mathematical Olympiad had just been taken by a team using their DeepSeekMath-7B model, solving 29 out of 50 private test questions «less challenging than those in the IMO but at the level of IMO preselection»; Terence Tao finds it «somewhat higher than expected» (he is on the AIMO Advisory Committee, along with his fellow Fields medalist Timothy Gowers).
The next three teams entered with this model as well.
I. The shape of the game board
To provide some context, here's an opinionated recap of AI trends since last year. I will be focusing exclusively on LLMs, as that's what matters (image gen, music gen, TTS etc largely are trivial conveniences, and other serious paradigms seem to be in their embryonic stage or in deep stealth).
- We have barely advanced in true out-of-distribution reasoning/understanding relative to the original «Sparks of AGI» GPT-4 (TheDag, me); GPT-4-04-29 and Sonnet 3.5 were the only substantial – both minor – steps forward, Gemini was a catch-up effort, and nobody else has yet credibly reached the same tier. We have also made scant progress towards consensus on whether that-which-LLMs-do is «truly» reasoning or understanding; sensible people have recoursed to something like «it's its own kind of mind, and hella useful».
- Meanwhile there's been a great deal of progress in scaffolding (no more babyAGI/AutoGPT gimmicry, now agents are climbing up the genuinely hard SWE-bench), code and math skills, inherent robustness in multi-turn interactions and responsiveness to nuanced feedback (to the point that LLMs can iteratively improve sizable codebases – as pair programmers, not just fancy-autocomplete «copilots»), factuality, respect of prioritized system instructions, patching badly covered parts of the world-knowledge/common sense manifold, unironic «alignment» and ironing out Sydney-like kinks in deployment, integrating non-textual modalities, managing long contexts (merely usable 32K "memory" was almost sci-fi back then, now 1M+ with strong recall is table stakes at the frontier; with 128K mastered on a deeper level by many groups) and a fairly insane jump in cost-effectiveness – marginally driven by better hardware, and mostly by distilling from raw pretrained models, better dataset curation, low-level inference optimizations, eliminating architectural redundancies and discovering many "good enough" if weaker techniques (for example, DPO instead of PPO). 15 months ago,"$0.002/1000 tokens" for gpt-3.5-turbo seemed incredible; now we always count tokens by the million, and Gemini-Flash blows 3.5-turbo out of the water for half that, so hard it's not funny; and we have reason to believe it's still raking in >50% margins whereas OpenAI probably subsidized their first offerings (though in light of distilling and possibly other methods of compute reuse, it's hard to rigorously account for a model's capital costs now).
- AI doom discourse has continued to develop roughly as I've predicted, but with MIRI pivoting to evidence-free advocacy, orthodox doomerism getting routed as a scientific paradigm, more extreme holdovers from it («emergent mesaoptimizers! tendrils of agency in inscrutable matrices!») being wearily dropped by players who matter, and misuse (SB 1047 etc) + geopolitical angle (you've probably seen young Leopold) gaining prominence.
- The gap in scientific and engineering understanding of AI between the broader community and "the frontier" has shrunk since the debut of GPT-4 or 3.5, because there's too much money to be made in AI and only so much lead you can get out of having assembled the most driven AGI company. Back then, only a small pool of external researchers could claim to understand what the hell they did above the level of shrugging "well, scale is all you need" (wrong answer) or speculating about some simple methods like "train on copyrighted textbooks" (spiritually true); people chased rumors, leaks… Now it takes weeks at most to trace a yet another jaw-dropping magical demo to papers, to cook up a proof of concept, or even to deem the direction suboptimal; the other two leading labs no longer seem desperate, and we're in the second episode of Anthropic's comfortable lead.
- Actual, downloadable open AI sucks way less than I've lamented last July. But it still sucks. And that's really bad, since it sucks most in the dimension that matters: delivering value, in the basest sense of helping do work that gets paid. And the one company built on the promise of «decentralizing intelligence», which I had hope for, had proven unstable.
To be more specific, open source (or as some say now, given the secretiveness of full recipes and opacity of datasets, «open weights») AI has mostly caught up in «creativity» and «personality», «knowledge» and some measure of «common sense», and can be used for petty consumer pleasures or simple labor automation, but it's far behind corporate products in «STEM» type skills, that are in short supply among human employees too: «hard» causal reasoning, information integration, coding, math. (Ironically, I agree here with whining artists that we're solving domains of competence in the wrong order. Also it's funny how by default coding seems to be what LLMs are most suited for, as the sequence of code is more constrained by preceding context than natural language is).
To wit, Western and Eastern corporations alike generously feed us – while smothering startups – fancy baubles to tinker with, charismatic talking toys; as they rev up self-improvement engines for full cycle R&D, the way imagined by science fiction authors all these decades ago, monopolizing this bright new world. Toys are getting prohibitively expensive to replicate, with reported pretraining costs up to ≈$12 million and counting now. Mistral's Mixtral/Codestral, Musk's Grok-0, 01.Ai's Yi-1.5, Databricks' DBRX-132B, Alibaba's Qwens, Meta's fantastic Llama 3 (barring the not-yet-released 405B version), Google's even better Gemma 2, Nvidia's massive Nemotron-340B – they're all neat. But they don't even pass for prototypes of engines you can hop on and hope to ride up the exponential curve. They're too… soft. And not economical for their merits.
Going through our archive, I find this year-old analysis strikingly relevant:
I think successful development of a trusted open model rivaling chatgpt in capability is likely in the span of a year, if people like you, who care about long-term consequences of lacking access to it, play their cards reasonably well. […] Companies whose existence depends on the defensibility of the moat around their LM-derived product will tend to structure the discourse around their product and technology to avoid even the fleeting perception of being a feasibly reproducible commodity.
That's about how it went. While the original ChatGPT, that fascinating demo, is commodified now, competitive product-grade AI systems are not, and companies big and small still work hard to maintain the impression that it takes
- some secret sauce (OpenAI, Anthropic)
- work of hundreds of Ph.Ds (Deepmind)
- vast capital and compute (Meta)
- "frontier experience" (Reka)
– and even then, none of them have felt secure enough yet to release a serious threat to the other's proprietary offers.
I don't think it's a big exaggerion to say that the only genuine pattern breaker – presciently mentioned by me here – is DeepSeek, the company that has single-handedly changed – a bit – my maximally skeptical spring'2023 position on the fate of China in the AGI race.
II. Deep seek what?
AGI, I guess. Their Twitter bio states only: «Unravel the mystery of AGI with curiosity. Answer the essential question with long-termism». It is claimed by the Financial Times that they have a recruitment pitch «We believe AGI is the violent beauty of model x data x computing power. Embark on a ‘deep quest’ with us on the journey towards AGI!» but other than that nobody I know of has seen any advertisement or self-promotion from them (except for like 70 tweets in total, all announcing some new capability or responding to basic user questions about license), so it's implausible that they're looking for attention or subsidies. Their researchers maintain near-perfect silence online. Their – now stronger and cheaper – models tend to be ignored in comparisons by Chinese AI businesses and users. As mentioned before, one well-informed Western ML researcher has joked that they're the bellwether for «the number of foreign spies embedded in the top labs».
FT also says the following of their parent company:
Its funds have returned 151 per cent, or 13 per cent annualised, since 2017, and were achieved in China’s battered domestic stock market. The country’s benchmark CSI 300 index, which tracks China’s top 300 stocks, has risen 8 per cent over the same time period, according to research provider Simu Paipai.
In February, Beijing cracked down on quant funds, blaming a stock market sell-off at the start of the year on their high-speed algorithmic trading. Since then, High-Flyer’s funds have trailed the CSI 300 by four percentage points.
[…] By 2021, all of High-Flyer’s strategies were using AI, according to manager Cai Liyu, employing strategies similar to those pioneered by hugely profitable hedge fund Renaissance Technologies. “AI helps to extract valuable data from massive data sets which can be useful for predicting stock prices and making investment decisions,” …
Cai said the company’s first computing cluster had cost nearly Rmb200mn and that High Flyer was investing about Rmb1bn to build a second supercomputing cluster, which would stretch across a roughly football pitch-sized area. Most of their profits went back into their AI infrastructure, he added. […] The group acquired the Nvidia A100 chips before Washington restricted their delivery to China in mid-2022.
“We always wanted to carry out larger-scale experiments, so we’ve always aimed to deploy as much computational power as possible,” founder Liang told Chinese tech site 36Kr last year. “We wanted to find a paradigm that can fully describe the entire financial market.”
In a less eclectic Socialist nation this would've been sold as Project Cybersyn or OGAS. Anyway, my guess is they're not getting subsidies from the Party any time soon.
They've made a minor splash in the ML community eight months ago, in late October, releasing an unreasonably strong Deepseek-Coder. Yes, in practice an awkward replacement for GPT-3.5, yes, contaminated with test set, which prompted most observers to discard it as a yet another Chinese fraud. But it proved to strictly dominate hyped-up things like Meta's CodeLLaMA and Mistral's Mixtral 8x7b in real-world performance, and time and again proved to be the strongest open baseline in research papers. On privately designed, new benchmarks like this fresh one from Cohere it's clear that they did get to parity with OpenAI's workhorse model, right on the first public attempt – as far as coding is concerned.
On top of that, they shared a great deal of information about how: constructing the dataset from Github, pretraining, finetuning. The paper was an absolute joy to read, sharing even details on unsuccessful experiments. It didn't offer much in the way of novelty; I evaluate it as a masterful, no-unforced-errors integration of fresh (by that point) known best practices. Think about your own field and you'll probably agree that even this is a high bar. And in AI, it is generally the case that either you get a great model with «we trained it on some text… probably» tech report (Mistral, Google), or a mediocre one accompanied by a fake-ass novel full of jargon (every second Chinese group). Still, few cared.
Coder was trained, it seems, using lessons of the less impressive Deepseek-LLM-67B (even so, it was roughly Meta's LLaMA-2-70B peer that also could code; a remarkable result for a literally-who new team), which somehow came out a month after. Its paper (released even later still) was subtitled «Scaling Open-Source Language Models with Longtermism». I am not sure if this was some kind of joke at the expense of effective altruists. What they meant concretely was the following:
Over the past few years, LLMs … have increasingly become the cornerstone and pathway to achieving Artificial General Intelligence (AGI). … Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source LMs with a long-term perspective.
- …Soon, we will release our technique reports in code intelligence and Mixture-of-Experts(MoE), respectively. They show how we create high-quality code data for pre-training, and design a sparse model to achieve dense model performance.
- At present, we are constructing a larger and improved dataset for the upcoming version of DeepSeek LLM. We hope the reasoning, Chinese knowledge, math, and code capabilities will be significantly improved in the next version.
- Our alignment team is dedicated to studying ways to deliver a model that is helpful, honest, and safe to the public. Our initial experiments prove that reinforcement learning could boost model complex reasoning capability.
…I apologize for geeking out. All that might seem normal enough. But, a) they've fulfilled every one of those objectives since then. And b) I've read a great deal of research papers and tech reports, entire series from many groups, and I don't remember this feeling of cheerful formidability. It's more like contemplating the dynamism of SpaceX or Tesla than wading through a boastful yet obscurantist press release. It is especially abnormal for a Mainland Chinese paper to be written like this – with friendly confidence, admitting weaknesses, pointing out errors you might repeat, not hiding disappointments behind academese word salad; and so assured of having a shot in an honest fight with the champion.
In the Coder paper, they conclude:
…This advancement underscores our belief that the most effective code-focused Large Language Models (LLMs) are those built upon robust general LLMs. The reason is evident: to effectively interpret and execute coding tasks, these models must also possess a deep understanding of human instructions, which often come in various forms of natural language. Looking ahead, our commitment is to develop and openly share even more powerful code-focused LLMs based on larger-scale general LLMs.
In the Mixture-of-Experts paper (8th January), they've shown themselves capable of novel architectural research too, introducing a pretty ingenuous «fine-grained MoE with shared experts» design with the objective of «Ultimate Expert Specialization» and economical inference: «DeepSeekMoE 145B significantly outperforms Gshard, matching DeepSeek 67B with 28.5% (maybe even 14.6%) computation». For those few who noticed it, this seemed a minor curiosity, or just bullshit.
On 5th February, they've dropped DeepSeekMath,of which I've already spoken: «Approaching Mathematical Reasoning Capability of GPT-4 with a 7B Model». Contra the usual Chinese pattern, it wasn't a lie; no, you couldn't in normal use get remotely as good results from it, but in some constrained regimes… The project itself was a mix of most of the previous steps: sophisticated (and well-explained) data harvesting pipeline, scaling laws experiments, further «longtermist» continued pretraining from Coder-7B-1.5 which itself is a repurposed LLM-7B, and the teased reinforcement learning approach. Numina, winners of AIMO, say «We also experimented with applying our SFT recipe to larger models like InternLM-20B, CodeLama-33B, and Mixtral-8x7B but found that (a) the DeepSeek 7B model is very hard to beat due to its continued pretraining on math…».
In early March they released DeepSeek-VL: Towards Real-World Vision-Language Understanding, reporting some decent results and research on building multimodal systems, and again announcing new plans: «to scale up DeepSeek-VL to larger sizes, incorporating Mixture of Experts technology».
III. Frontier minor league
This far, it's all been preparatory R&D, shared openly and explained eagerly yet barely noticed by anyone (except that the trusty Coder still served as base for labs like Microsoft Research to experiment on): utterly overshadowed in discussions by Alibaba, Meta, Mistral, to say nothing of frontier labs.
But on May 6th, 2024, the pieces began to fall into place. They released «DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model», which subsumed all aforementioned works (except VL).
It's… unlike any other open model, to the point you could believe it was actually made by some high-IQ finance bros from first principles. Its design choices are exquisite, just copying minor details can substantially improve on typical non-frontier efforts. It pushes further their already unorthodox MoE and tops it off with a deep, still poorly understood modification to the attention mechanism (Multi-head Latent Attention, or MLA). It deviates from industry-standard rotary position embeddings to accomodate the latter (a fruit of collaboration with RoPE's inventor). It's still so unconventional that we are only beginning to figure out how to run it properly (they don't share their internal pipeline, which is optimized for hardware they can access given American sanctions). But in retrospect, it's the obvious culmination of the vision announced with those first model releases and goofy tweets, probably a vision not one year old, and yet astonishingly far-sighted – especially given how young their star researchers are. But probably it's mundane in the landscape of AI that's actually used; I suspect it's close to how Sonnet 3.5 or Gemini 1.5 Pro work on the inside. It's just that the open-source peasants are still mucking around with stone age dense models on their tiny consumer GPUs.
I understand I might already be boring you out of your mind, but just to give you an idea of how impressive this whole sequence is, here's a 3rd April paper for context:
Recent developments, such as Mixtral (Jiang et al., 2024), DeepSeek-MoE (Dai et al., 2024), spotlight Mixture-of-Experts (MoE) models as a superior alternative to Dense Transformers. An MoE layer works by routing each input token to a selected group of experts for processing. Remarkably, increasing the number of experts in an MoE model (almost) does not raise the computational cost, enabling the model to incorporate more knowledge through extra parameters without inflating pre-training expenses… Although our findings suggest a loss-optimal configuration with Emax experts, such a setup is not practical for actual deployment. The main reason is that an excessive number of experts makes the model impractical for inference. In contrast to pretraining, LLM inference is notably memory-intensive, as it requires storing intermediate states (KV-cache) of all tokens. With more experts, the available memory for storing KV caches is squeezed. As a result, the batch size – hence throughput – decreases, leading to increased cost per query. … We found that MoE models with 4 or 8 experts exhibit more efficient inference and higher performance compared to MoE models with more experts. However, they necessitate 2.4x-4.3x more training budgets to reach the same performance with models with more experts, making them impractical from the training side.
This is basically where Mistral.AI, the undisputed European champion with Meta and Google pedigree (valuation $6.2B), the darling of the opensource community, stands.
And yet, apparently DeepSeek have found a way to get out of the bind. «4 or 8»? They scale to 162 experts, reducing active parameters to 21B, cutting down pretraining costs by 42.5% and increasing peak generation speed by 5.76x; and they scale up the batch size via compressing the KV cache by like 15 times with a bizarre application of low-rank projections and dot attention; and while doing so they cram in 3x more attention heads than any model this size has any business having (because their new attention decouples number of heads from cache size), and so kick the effective «thinking intensity» up a notch, beating the gold standard «Multihead attention» everyone has been lousily approximating; and they use a bunch of auxiliary losses to make the whole thing maximally cheap to use on their specific node configuration.
But the cache trick is pretty insane. The hardest-to-believe, for me, part of the whole thing. Now, 2 months later, we know that certain Western groups ought to have reached the same Pareto frontier, just with different (maybe worse, maybe better) tradeoffs. But those are literally inventors and/or godfathers of the Transformer – Noam Shazeer's CharacterAI, Google Deepmind's Gemini line… This is done by folks like this serious-looking 5th year Ph.D student, in under a year!
As a result, they:
- use about as much compute on pretraining as Meta did on Llama-3-8B, an utter toy in comparison (maybe worth $2.5 million for them); 1/20th of GPT-4.
- Get a 236B model that's about as good across the board as Meta's Llama-3-70B (≈4x more compute), which has the capacity – if not the capability – of mid-range frontier models (previous Claude 3 Sonnet; GPT-4 on a bad day).
- Can serve it at around the price of 8B, $0.14 for processing 1 million tokens of input and $0.28 for generating 1 million tokens of output (1 and 2 Yuan), on previous-gen hardware too.
- …and still take up to 70%+ gross margins, because «On a single node with 8 H800 GPUs, DeepSeek-V2 achieves a generation throughput exceeding 50K tokens per second… In addition, the prompt input throughput of DeepSeek-V2 exceeds 100K tokens per second», and the going price for such nodes is ≤$15/hr. That's $50 in revenue, for clarity. They aren't doing a marketing stunt.
- …and so they force every deep-pocketed mediocre Chinese LLM vendor – Alibaba, Zhipu and all – to drop prices overnight, now likely serving at a loss.
Now, I am less sure about some parts of this story; but mostly it's verifiable.
I can see why an American, or a young German like Leopold, would freak out about espionage. The thing is, their papers are just too damn good and too damn consistent over the entire period if you look back (as I did), so «that's it, lock the labs» or «haha, no more tokens 4 u» is most likely little more than racist cope for the time being. The appropriate reaction would be more akin to «holy shit Japanese cars are in fact good».
Smart people (Jack Clark from Anthropic, Dylan Patel of Semianalysis) immediately take note. Very Rational people clamoring for AI pause (TheZvi) sneer and downplay: «This is who we are worried about?» (as he did before, and before). But it is still good fun. Nothing extreme. There slowly begin efforts at adoption: say, Salesforce uses V2-Chat to create synthetic data to finetune small Deepseek-Coder V1s to outperform GPT-4 on narrow tasks. Mostly nobody cares.
The paper ends in the usual manner of cryptic comments and commitments:
We thank all those who have contributed to DeepSeek-V2 but are not mentioned in the paper. DeepSeek believes that innovation, novelty, and curiosity are essential in the path to AGI.
DeepSeek will continuously invest in open-source large models with longtermism, aiming to progressively approach the goal of artificial general intelligence.
• In our ongoing exploration, we are dedicated to devising methods that enable further scaling up MoE models while maintaining economical training and inference costs. The goal of our next step is to achieve performance on par with GPT-4 in our upcoming release.
In the Appendix, you can find a lot of curious info, such as:
During pre-training data preparation, we identify and *filter out contentious content, such as values influenced by regional cultures, to avoid our model exhibiting unnecessary subjective biases on these controversial topics. Consequently, we observe that DeepSeek-V2 performs slightly worse on the test sets that are closely associated with specific regional cultures. For example, when evaluated on MMLU, although DeepSeek-V2 achieves comparable or superior performance on the majority of testsets compared with its competitors like Mixtral 8x22B, it still lags behind on the Humanity-Moral subset, which is mainly associated with American values.
Prejudices of specific regional cultures aside, though, it does have values – true, Middle Kingdom ones, such as uncritically supporting the Party line and adherence to Core Values Of Socialism (h/t @RandomRanger). The web version will also delete the last message if you ask something too clever about Xi or Tiananmen or… well, nearly the entirety of usual things Americans want to talk to Chinese coding-oriented LLMs about.
And a bit earlier, this funny guy from the team presented at Nvidia's GTC24 with the product for the general case – «culturally sensitive», customizable alignment-on-demand: «legality of rifle» for the imperialists, illegality of Tibet separatism for the civilized folk. Refreshingly frank.
But again, even that was just a preparatory.
IV. Coming at the king
Roughly 40 days later they release DeepSeek-V2-Coder: Breaking the Barrier of Closed-Source Models in Code Intelligence, where they return to the strategy announced at the very start: they take an intermediate checkpoint of V2, and push it harder and further on the dataset enriched with code and math (that that've continued to expand and refine), for 10.2 trillion tokens total. Now this training run is 60% more expensive than Llama-3-8B (still a pittance by modern standards). It also misses out on some trivia knowledge and somehow becomes even less charismatic. It's also not a pleasant experience because the API runs very slowly, probably from congestion (I guess Chinese businesses are stingy… or perhaps DeepSeek is generating a lot of synthetic data for next iterations). Anons on 4chan joke that it's «perfect for roleplaying with smart, hard-to-get characters».
More importantly though, it demolishes Llama-3-70B on every task that takes nontrivial intelligence; bests Claude 3 Opus on coding and math throughout, Gemini 1.5-Pro on most coding assistance, and trades blows with the strongest GPT-4 variants. Of course it's the same shape and the same price, which is to say, up to 100 times cheaper than its peers… more than 100 times, in the case of Opus. Still a bitch to run, but it turns out they're selling turnkey servers. In China, of course. To boot, they rapidly shipped running code in browser (a very simple feature but going most of the way to Claude Artifacts that wowed people do much), quadrupled context length without price changes (32k to 128k) and now intend to add context caching that Google boasts of as some tremendous Gemini breakthrough. They have... Impressive execution.
Benchmarks, from the most sophisticated and hard to hack to the most bespoke and obscure, confirm that it's «up there».
- Aider (2nd, 1st at release)
- LMSYS Arena (low on Overall, but 5th rank on Coding and 7 ranks above Google and Meta's open source alternatives, respectively 11th and 3+ ranks above on Hard subsample)
- Arena-Hard-Auto(7th, surprisingly 2 more Chinese models narrowly get ahead)
- %compilable Golang programs (2nd)
- Livebench (7th, by virtue of being 5th-6th in coding and reasoning and 2nd in Math; everyone above is OpenAI/Anthropic)
- LiveCodeBench (4th, same order)
- BigCodeBench(2nd)
- Gaokao-Math, released days before its deployment (roughly above GPT-4o)
Etc etc, and crucially, users report similar impressions:
So I have pegged deepseek v2 coder against sonnet 3.5 and gpt4o in my coding tasks and it seems to be better than gpt4o (What is happening at OpenAI) and very similar to Sonnet 3.5. The only downside is the speed, it's kinda slow. Very good model and the price is unbeatable.
I had the same experience, this is a very good model for serious tasks. Sadly the chat version is very dry and uncreative for writing. Maybe skill issue, I do not know. It doesn't feel slopped, it's just.. very dry. It doesn't come up with things.
Some frustrating weak points, but they know of those, and conclude:
Although DeepSeek-Coder-V2 achieves impressive performance on standard benchmarks, we find that there is still a significant gap in instruction-following capabilities compared to current state-of-the-art models like GPT-4 Turbo. This gap leads to poor performance in complex scenarios and tasks such as those in SWEbench. […] In the future, we will focus more on improving the model’s instruction-following capabilities…
Followed by the list of 338 supported languages.
Well-read researchers say stuff like
DeepSeek-Coder-V2 is by far the best open-source math (+ coding) model, performing on par with GPT4o w/o process RM or MCTS and w/ >20x less training compute. Data contamination doesn't seem to be a concern here. Imagine about what this model could achieve with PRM, MCTS, and other yet-to-be-released agentic exploration methods. Unlike GPT4o, you can train this model further. It has the potential to solve Olympiad, PhD and maybe even research level problems, like the internal model a Microsoft exec said to be able to solve PhD qualifying exam questions».
Among the Rational, there is some cautious realization («This is one of the best signs so far that China can do something competitive in the space, if this benchmark turns out to be good»), in short order giving way to more cope : «Arena is less kind to DeepSeek, giving it an 1179, good for 21st and behind open model Gemma-2-9B».
And one more detail: A couple weeks ago, they released code and paper on Expert-Specialized Fine-Tuning, «which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the efficiency, but also matches or even surpasses the performance of full-parameter fine-tuning … by showing less performance degradation [in general tasks]». It seems to require that «ultimate expert specialization» design of theirs, with its supporting beam of generalist modules surrounded by meaningfully task-specific shards, to automatically select only the parts pertaining to some target domain; and this isn't doable with traditional dense of MoE designs. Once again: confident vision, bearing fruit months later. I would like to know who's charting their course, because they're single-handedly redeeming my opinion of the Chinese AI ecosystem and frankly Chinese culture.
V. Where does this leave us?
This might not change much. Western closed AI compute moat continues to deepen, DeepSeek/High-Flyer don't have any apparent privileged access to domestic chips, and other Chinese groups have friends in the Standing Committee and in the industry, so realistically this will be a blip on the radar of history. A month ago they've precluded a certain level of safetyist excess and corporate lock-in that still seemed possible in late 2023, when the argument that public availability of ≈GPT-4 level weights (with the main imaginary threat vectors being coding/reasoning-bottlenecked) could present intolerable risks was discussed in earnest. One-two more such leaps and we're… there, for the vague libertarian intuition of «there» I won't elucidate now. But they're already not sharing the silently updated Deepseek-V2-Chat (that somewhat improved its reasoning, getting closer to the Coder), nor the promised materials on DeepSeek-Prover (a quiet further development of their mathematical models line). Maybe it's temporary. Maybe they've arrived to where they wanted to be, and will turtle up like Stability and Mistral, and then likely wither away.
Mostly, I honestly just think it's remarkable that we're getting an excellent, practically useful free model with lowkey socialist sensibilities. Sadly, I do not foresee that this will inspire Western groups to accelerate open source and leave them in the dust. As Google says in Gemma-2 report:
Despite advancements in capabilities, we believe that given the number of larger and more powerful open models, this release will have a negligible effect on the overall risk landscape.
Less charitably, Google is not interested in releasing anything you might use to enhance your capabilities and become less dependent on Google or other «frontier company», and will only release it if you are well able of getting better stuff elsewhere. In my view, this is closer to the core value of Socialism than withholding info about Xinjiang reeducation camps.
I remain agnostic about the motivations and game plan of DeepSeek, but I do hope they'll maintain this policy of releasing models «with longtermism», as it were. We don't have many others to rely on.
Edits: minor fixes
As I've been arguing for some time, the culture war's most important front will be about AI; that's more pleasant to me than the tacky trans vs trads content, as it returns us to the level of philosophy and positive actionable visions rather than peculiarly American signaling ick-changes, but the stakes are correspondingly higher… Anyway, Forbes has doxxed the founder of «e/acc», irreverent Twitter meme movement opposing attempts at regulation of AI development which are spearheaded by EA. Turns out he's a pretty cool guy eh.
Who Is @BasedBeffJezos, The Leader Of The Tech Elite’s ‘E/Acc’ Movement?
…At first blush, e/acc sounds a lot like Facebook’s old motto: “move fast and break things.” But Jezos also embraces more extreme ideas, borrowing concepts from “accelerationism,” which argues we should hasten the growth of technology and capitalism at the expense of nearly anything else. On X, the platform formally known as Twitter where he has 50,000 followers, Jezos has claimed that “institutions have decayed beyond the point of salvaging and that the media is a “vector for cybernetic control of culture.”
Alarmed by this extremist messaging, «the media» proceeds to… harness the power of an institution associated with the Department of Justice to deanonymize him, with the explicit aim to steer the cultural evolution around the topic:
Forbes has learned that the Jezos persona is run by a former Google quantum computing engineer named Guillaume Verdon who founded a stealth AI hardware startup Extropic in 2022. Forbes first identified Verdon as Jezos by matching details that Jezos revealed about himself to publicly available facts about Verdon. A voice analysis conducted by Catalin Grigoras, Director of the National Center for Media Forensics, compared audio recordings of Jezos and talks given by Verdon and found that it was 2,954,870 times more likely that the speaker in one recording of Jezos was Verdon than that it was any other person. Forbes is revealing his identity because we believe it to be in the public interest as Jezos’s influence grows.
That's not bad because Journalists, as observed by @TracingWoodgrains, are inherently Good:
(Revealing the name behind an anonymous account of public note is not “doxxing,” which is an often-gendered form of online harassment that reveals private information — like an address or phone number — about a person without consent and with malicious intent.)
(That's one creative approach to encouraging gender transition, I guess).
Now to be fair, this is almost certainly parallel construction narrative – many people in the SV knew Beff's real persona, and as of late he's been very loose with opsec, funding a party, selling merch and so on. Also, the forced reveal will probably help him a great deal – it's harder to dismiss the guy as some LARPing shitposter or a corporate shill pandering to VCs (or as @Tomato said, running «an incredibly boring b2b productivity software startup») when you know he's, well, this. And this too.
Forbes article itself doesn't go very hard on Beff, presenting him as a somewhat pretentious supply-side YIMBY, an ally to Marc Andreessen, Garry Tan and such; which is more true of Beff's followers than the man himself. The more potentially damaging (to his ability to draw investment) parts are casually invoking the spirit of Nick Land and his spooky brand of accelerationism (not unwarranted – «e/acc has no particular allegiance to the biological substrate for intelligence and life, in contrast to transhumanism; in order to spread to the stars, the light of consciousness/intelligence will have to be transduced to non-biological substrates» Beff says in his manifesto), and citing some professors of «communications» and «critical theory» who are just not very impressed with the whole technocapital thing. At the same time, it reminds the reader of EA's greatest moment (no not the bed nets).
Online, Beff confirms being Verdon:
I started this account as a means to spread hope, optimism, and a will to build the future, and as an outlet to share my thoughts despite to the secretive nature of my work… Around the same time as founding e/acc, I founded @Extropic_AI. A deep tech startup where we are building the ultimate substrate for Generative AI in the physical world by harnessing thermodynamic physics. Ideas simmering while inventing a this paradigm of computing definitely influenced the initial e/acc writings. I very much look forward to sharing more about our vision for the technology we are building soon. In terms of my background, as you've now learned, my main identity is @GillVerd. I used to work on special projects at the intersection of physics and AI at Alphabet, X and Google. Before this, I was a theoretical physicist working on information theory and black hole physics. Currently working on our AI Manhattan project to bring fundamentally new computing to the world with an amazing team of physics and AI geniuses, including my former TensorFlow Quantum co-founder @trevormccrt1 as CTO. Grateful every day to get to build this technology I have been dreaming of for over 8 years now with an amazing team.
And Verdon confirms the belief in Beffian doctrine:
Civilization desperately needs novel cultural and computing paradigms for us to achieve grander scope & scale and a prosperous future. I strongly believe thermodynamic physics and AI hold many of the answers we seek. As such, 18 months ago, I set out to build such cultural and computational paradigms.
I am fairly pessimistic about Extropic for reasons that should be obvious enough to people who've been monitoring the situation with DL compute startups and bottlenecks, so it may be that Beff's cultural engineering will make a greater impact than Verdon's physical one. Ironic, for one so contemptuous of wordcels.
Maturation of e/acc from a meme to a real force, if it happens (and as feared on Alignment Forum, in the wake of OpenAI coup-countercoup debacle), will be part of a larger trend, where the quasi-Masonic NGO networks of AI safetyists embed themselves in legacy institutions to procure the power of law and privileged platforms, while the broader organic culture and industry develops increasingly potent contrarian antibodies to their centralizing drive. Shortly before the doxx, two other clusters in the AI debate have been announced.
First one I'd mention is d/acc, courtesy of Vitalik Buterin; it's the closest to acceptable compromise that I've seen. It does not have many adherents yet but I expect it to become formidable because Vitalik is.
Across the board, I see far too many plans to save the world that involve giving a small group of people extreme and opaque power and hoping that they use it wisely. And so I find myself drawn to a different philosophy, one that has detailed ideas for how to deal with risks, but which seeks to create and maintain a more democratic world and tries to avoid centralization as the go-to solution to our problems. This philosophy also goes quite a bit broader than AI, and I would argue that it applies well even in worlds where AI risk concerns turn out to be largely unfounded. I will refer to this philosophy by the name of d/acc.
The "d" here can stand for many things; particularly, defense, decentralization, democracy and differential. First, think of it about defense, and then we can see how this ties into the other interpretations.
[…] The default path forward suggested by many of those who worry about AI essentially leads to a minimal AI world government. Near-term versions of this include a proposal for a "multinational AGI consortium" ("MAGIC"). Such a consortium, if it gets established and succeeds at its goals of creating superintelligent AI, would have a natural path to becoming a de-facto minimal world government. Longer-term, there are ideas like the "pivotal act" theory: we create an AI that performs a single one-time act which rearranges the world into a game where from that point forward humans are still in charge, but where the game board is somehow more defense-favoring and more fit for human flourishing.
The main practical issue that I see with this so far is that people don't seem to actually trust any specific governance mechanism with the power to build such a thing. This fact becomes stark when you look at the results to my recent Twitter polls, asking if people would prefer to see AI monopolized by a single entity with a decade head-start, or AI delayed by a decade for everyone… The size of each poll is small, but the polls make up for it in the uniformity of their result across a wide diversity of sources and options. In nine out of nine cases, the majority of people would rather see highly advanced AI delayed by a decade outright than be monopolized by a single group, whether it's a corporation, government or multinational body. In seven out of nine cases, delay won by at least two to one. This seems like an important fact to understand for anyone pursuing AI regulation.
[…] my experience trying to ensure "polytheism" within the Ethereum ecosystem does make me worry that this is an inherently unstable equilibrium. In Ethereum, we have intentionally tried to ensure decentralization of many parts of the stack: ensuring that there's no single codebase that controls more than half of the proof of stake network, trying to counteract the dominance of large staking pools, improving geographic decentralization, and so on. Essentially, Ethereum is actually attempting to execute on the old libertarian dream of a market-based society that uses social pressure, rather than government, as the antitrust regulator. To some extent, this has worked: the Prysm client's dominance has dropped from above 70% to under 45%. But this is not some automatic market process: it's the result of human intention and coordinated action.
[…] if we want to extrapolate this idea of human-AI cooperation further, we get to more radical conclusions**. Unless we create a world government powerful enough to detect and stop every small group of people hacking on individual GPUs with laptops, someone is going to create a superintelligent AI eventually - one that can think a thousand times faster than we can - and no combination of humans using tools with their hands is going to be able to hold its own against that. And so we need to take this idea of human-computer cooperation much deeper and further. A first natural step is brain-computer interfaces.…
etc. I mostly agree with his points. By focusing on the denial of winner-takes-all dynamics, it becomes a natural big tent proposal and it's already having effect on the similarly big tent doomer coalition, pulling anxious transhumanists away from the less efficacious luddites and discredited AI deniers.
The second one is «AI optimism» represented chiefly by Nora Belrose from Eleuther and Qiuntin Pope (whose essays contra Yud 1 and contra appeal to evolution as an intuition pump 2 I've been citing and signal-boosting for next to a year now; he's pretty good on Twitter too). Belrose is in agreement with d/acc; and in principle, I think this one is not so much a faction or a movement as the endgame to the long arc of AI doomerism initiated by Eliezer Yudkowsky, the ultimate progenitor of this community, born of the crisis of faith in Yud's and Bostrom's first-principles conjectures and entire «rationality» in light of empirical evidence. Many have tried to attack the AI doom doctrine from the outside (eg George Hotz), but only those willing to engage in the exegesis of Lesswrongian scriptures can sway educated doomers. Other actors in, or close to this group:
- Matthew Barnett with his analysis of goalpost-movement by MIRI, such as on factory-running benchmark and value misspecification thesis, and strengths of optimistic paradigms like Drexler's CAIS model.
- Alex Turner, who had written, arguably, two strongest and most popular formal proofs of instrumental convergence to power-seeking in AI agents 1 2, but has since fallen from grace, regrets his work and thinks deceptive alignment with LLMs is pretty much impossible.
- 1a3orn who is mainly concerned about centralization of power and opportunistic exploitation of AI risk narratives (also recommended: on Hansonian position in the FOOM debate).
- Beren Millidge the former head of research at Conjecture, Connor Leahy's extreme doomer company which has pivoted fully to advocacy and is gaining pull among British elites; over the last 1-2 years he has concluded that almost all of the MIRI-style assumptions 1 2 and their policy implications are confused.
- John David Pressman who's just a good thinker and AI researcher at Stability.
- Various other renegades and doubters like Kaj Sotala dunking on the evolution appeal, Zach M. Davis debating Yud-like «Doomimir», Nostalgebraist, arguably many scientists with P(doom)≤25% (and if you press them, ≈0% for their own AGI research program) like Rohin Shah. To an extent, even Paul Christiano (although he is in favor of decelerating; there are speculations that it's mostly due to being married to EA).
Optimists claim:
The last decade has shown that AI is much easier to control than many had feared. Today’s brain-inspired neural networks inherit human common sense, and their behavior can be molded to our preferences with simple, powerful algorithms. It’s no longer a question of how to control AI at all, but rather who will control it.
As optimists, we believe that AI is a tool for human empowerment, and that most people are fundamentally good. We strive for a future in which AI is distributed broadly and equitably, where each person is empowered by AIs working for them, under their own control. To this end, we support the open-source AI community, and we oppose attempts to centralize AI research in the hands of a small number of corporations in the name of “safety.” Centralization is likely to increase economic inequality and harm civil liberties, while doing little to prevent determined wrongdoers. By developing AI in the open, we’ll be able to better understand the ways in which AI can be misused and develop effective defense mechanisms.
So in terms of a political compass:
- AI Luddites, reactionaries, job protectionists and woke ethics grifters who demand pause/stop/red tape/sinecures (bottom left)
- plus messianic Utopian EAs who wish for a moral singleton God, and state/intelligence actors making use of them (top left)
- vs. libertarian social-darwinist and posthumanist e/accs often aligned with American corporations and the MIC (top right?)
- and minarchist/communalist transhumanist d/accs who try to walk the tightrope of human empowerment (bottom right?)
(Not covered: Schmidhuber, Sutton& probably Carmack as radically «misaligned» AGI successor species builders, Suleyman the statist, LeCun the Panglossian, Bengio&Hinton the naive socialists, Hassabis the vague, Legg the prophet, Tegmark the hysterical, Marcus the pooh-pooher and many others).
This compass will be more important than the default one as time goes on. Where are you on it?
As an aside: I recommend two open LLMs above all others. One is OpenHermes 2.5-7B, the other is DeepSeek-67B (33b-coder is OK too). Try them. It's not OpenAI, but it's getting closer and you don't need to depend on Altman's or Larry Summers' good graces to use them. With a laptop, you can have AI – at times approaching human level – anywhere. This is irreversible.
I've been wrong, again, pooh-poohing another Eurasian autocracy. Or so it seems.
On 29 August 2023, to great jubilation of Chinese netizens («the light boat has passed through a thousand mountains!», they cry), Huawei has announced Mate 60 and 60 Pro; the formal launch is scheduled for September 25th, commemorating the second anniversary of return of Meng Wanzhou, CFO and daughter of Huawei's founder, from her detainment in Canada. Those are nice phones of course but, specs-wise, unimpressive, as far as flagships in late 2023 go (on benchmarks, score like 50-60% of the latest iPhone while burning peak 13W so 200% of power). Now they're joined by Mate X5.
The point, however, is that they utilize Huawei's own SoC, Hisilicon Kirin 9000S, not only designed but produced in the Mainland; it even uses custom cores that inherit simultaneous multithreading from their server line (I recommend this excellent video review, also this benchmarking). Their provenance is not advertised, in fact it's not admitted at all, but now all reasonable people are in agreement that it's SMIC-Shanghai made, using their N+2 (7nm) process, with actual minimum metal pitch around 42 nm, energy efficiency at low frequencies close to Samsung's 4nm and far worse at high (overall capability in the Snapdragon 888 range, so 2020), transistor density on par with first-gen TSMC N7, maybe N7P (I'm not sure though, might well be 10% higher)… so on the border of what has been achieved with DUV (deep ultraviolet) and early EUV runs (EUV technology having been denied to China. As a side note, Huawei is also accused of building its own secret fabs).
It's also worse on net than Kirin 9000, their all-time peak achievement taped out across the strait in 2020, but it's… competitive. They apparently use self-aligned quad patterning, a DUV variant that's as finicky as it sounds, an absurd attempt to cheat optics and etch features many times smaller than the etching photons' wavelength (certain madmen went as high as 6x patterning; that said, even basic single-patterning EUV is insane and finicky, «physics experiment, not a production process»; companies on the level of Nikon exited the market in exasperation rather than pursue it; and it'll get worse). This trick was pioneered by Intel (which has failed at adopting EUV, afaik it's a fascinating corporate mismanagement story with as much strategic error as simple asshole behavior of individual executives) and is still responsible for their latest chips, though will be made obsolete in the next generations (the current node used to be called Intel's 10 nm Enhanced SuperFin, and was recently rebranded to Intel 7; note, however, that Kirin 9000S is a low-power part and requirements there are a bit more lax than in desktop/server processors). Long story short: it's 1.5-2 generations, 3-4 years behind the frontier of available devices, 5-6 years behind frontier production runs, 7-8 years after the first machines to make such chips at scale came onto market; but things weren't that much worse back then. We are, after all, in the domain of diminishing returns.
Here are the highlights from the first serious investigation, here are some leaks from it, here's the nice Asianometry overview (esp 3:50+), and the exhilarating, if breathlessly hawkish perspective of Dylan Patel, complete with detailed restrictions-tightening advice. Summarizing:
- This is possible because sanctions against China have tons of loopholes, and because ASML and other suppliers are not interested in sacrificing their business to American ambition. *
- Yes, it qualifies for 7nm in terms of critical dimensions. Yes, it's not Potemkin tulou, they likely have passable yields, both catastrophic and parametric (maybe upwards of 50% for this SoC, because low variance in stress-testing means they didn't feel the need to approve barely-functional chips, meaning there weren't too many defects) and so it's economically sustainable (might be better in that sense than e.g. Samsung's "5nm" or "4nm", because Samsung rots alive due to systemic management fraud) [I admit I doubt this point, and Dylan is known to be a hawk with motivated reasoning]. Based on known capex, they will soon be able to produce 30K wafers per month, which means 10s of millions of such chips soon (corroborated by shipment targets; concretely it's like 300 Kirins *29700 wafers so 8.9M/month, but the cycle is>1 month). And yes, they will scale it up further, and indeed they will keep polishing this tech tree and plausibly get to commercially viable "5nm" next - «the total process cost would only be ≈20% higher versus a 5nm that utilizes EUV» (probably 50%+ though).
- But more importantly: «Even with 50% yields, 30,000 WPM could support over 10 million Nvidia H100 GPU ASIC dies a year […] Remember GPT-4 was trained on ≈24,000 A100’s and Open AI will still have less than 1 million advanced GPUs even by the end of next year». Of course, Huawei already had been producing competitive DL accelerators back when they had access to EUV 7nm; even now I stumble upon ML papers that mention using those.
- As if all that were not enough, China simply keeps splurging billions on pretty good ML-optimized hardware, like Nvidia A/H800s, which abide with the current (toothless, as Patel argues) restrictions.
- But once again: on a bright (for Westerners) side, this means it's not so much Chinese ingenuity and industriousness (for example, they still haven't delivered a single ≤28nm lithography machine, though it's not clear if the one they're working on won't be rapidly upgraded for 20, 14, 10 and ultimately 7nm processes – after all, SMIC is currently procuring tools for «28nm», complying with sanctions, yet here we are), as it's the unpicked low-hanging fruit of trade restrictions. In fact, some Chinese doomers argue it's a specific allowance by the US Department of Commerce and overall a nothingburger, ie doesn't suggest willingness to produce more consequential things than gadgets for patriotic consumers. The usual suspects (Zeihan and his flock) take another view and smugly claim that China has once again shot itself in the foot while showing off, paper tiger, wolf warriors, only steals and copies etc.; and, the stated objective of the USG being «as large of a lead as possible», new crippling sanctions are inevitable (maybe from Patel's list). There exists a body of scholarship on semiconductor supply chain chokepoints which confirms these folks are not delusional – something as «simple» as high-end photoresist is currently beyond Chinese grasp, so the US can make use of a hefty stick.
All that being said, China does advance in on-shoring the supply chain: EDA, 28nm scanners, wafers etc.
* Note: Patel plays fast and loose with how many lithography machines exactly, and of what capacity, are delivered/serviced/ordered/shipping/planned/allowed, and it's the murkiest part in the whole narrative; for example he describes ASML's race-traitorous plans stretching to 2025-2030, but the Dutch and also the Japanese seem to already have began limiting sales of tools he lists as unwisely left unbanned, and so the August surge or imports may have been the last, and certainly most 2024+ sales are off the table I think.
All of this is a retreading of a discussion from over a year ago, when a less mature version of SMIC N7 process was used - also surreptitiously – for a Bitcoin mining ASIC, a simple, obscenely high-margin part 19.3mm² in size, which presumably would have been profitable to make even at pathetic yields, like 10%; the process back then was near-idential to TSMC N7 circa 2018-2019. 9000S is 107 mm² and lower-margin. Nvidia GH100, the new workhorse of cutting edge ML, made with 4nm TSMC node, is 814 mm²; as GPU chips are a strategic resource, it'd be sensible to subsidize their production (as it happens, H100 with its 98 MTr/mm² must be equally or a bit less dense than 9000S; A100, a perfectly adequate 7nm downgrade option, is at 65 MTr/mm² so we can be sure they'll be capable of making those, eg resurrecting Biren BR100 GPUs or things like Ascend 910). Citing Patel again, «Just like Apple is the guinea pig for TSMC process nodes and helps them ramp and achieve high yield, Huawei will likewise help SMIC in the same way […] In two years, SMIC will likely be able to produce large monolithic dies for AI and networking applications.» (In an aside, Patel laments the relative lack of gusto in strangling Chinese radio/sensor capabilities, which are more formidable and immediately scary than all that compute. However, this makes sense if we look at the ongoing chip trade war through the historical lens, with the reasonable objective being Chinese obsolescence a la what happened to the Soviet Union and its microelectronics, and arguably even Japan in the 80s, which is why ASML/Samsung/TSMC are on the map at all; Choyna military threat per se, except to Taiwan, being a distant second thought, if not a total pretext. This r/LessCredibleDefense discussion may be of interest).
So. I have also pooh-poohed the Chinese result back then, assuming that tiny crypto ASICs are as good as they will get within the bounds assigned to them, «swan song of Chinese industry», and won't achieve meaningful yields. Just as gwern de facto did in October 2022, predicting the slow death of Chinese industry in view of «Export Controls on Advanced Computing and Semiconductor Manufacturing Items to the PRC» (even mentioning the yellow bear meme). Just as I did again 4 months ago, saying to @RandomRanger «China will maybe have 7nm in 2030 or something». I maintain that it's plausible they won't have a fully indigenized supply chain for any 7nm process until 2030 (and/or will likewise fail with securing chains for necessary components other than processors: HBM, interposers etc), they may well fall below the capacity they have right now (reminder that not only do scanners break down and need consumables, but they can be remotely disabled), especially if restrictions keep ramping up and they'll keep making stupid errors, e.g. actually starting and failing an attempt at annexing Taiwan, or going for Cultural Revolution Round II: Zero Covid Boogaloo, or provoking an insurgency by force-feeding all primary school students gutter oil breakfasts… with absolute power, the possibilities are endless! My dissmissal was informed not by prejudice but years upon years of promises by Chinese industry and academia representatives to get to 7nm in 2 more weeks, and consistent failure and high-profile fraud (and in fact I found persuasive this dude's argument that by some non-absurd measures the gap has widened since the Mao's era; and there was all the graphene/quantum computing "leapfrogging" nonsense, and so on). Their actors haven't become appreciably better now.
But I won't pooh-pooh any more, because their chips have become better. I also have said: «AGI can be completed with already available hardware, and the US-led bloc has like 95% of it, and total control over means of production». This is still technically true but apparently not in a decisive way. History is still likely to repeat – that is, like the Qing China during the Industrial Revolution, like the Soviet Union in the transistor era, the nation playing catch-up will once again run into trade restrictions, fail at the domestic fundamental innovation and miss out on the new technological stage; but it is not set in stone. Hell, they may even get to EUV through that asinine 160m synchrotron-based electron beam thing – I mean, they are trying, though it still looks like ever more academic grift… but…
I have underestimated China and overestimated the West. Mea culpa. Alphanumericsprawl and others were making good points.
Where does this leave us?
It leaves us in the uncomfortable situation where China as a rival superpower will plausibly have to be defeated for real, rather then just sanctioned away or allowed to bog itself down in imperialist adventurism and incompetence. They'll have enough suitable chips, they have passable software, enough talent for 1-3 frontier companies, reams of data and their characteristically awkward ruthlessness applied to refining it (and as we've learned recently, high-quality data can compensate for a great disparity in compute). They are already running a few serious almost-OpenAI-level projects – Baidu's ERNIE, Alibaba's Tongyi Qianwen (maybe I've mentioned it already, but their Qwen-7B/VL are really good; seems like all groups in the race were obligated to release a small model for testing purposes), maybe also Tsinghua's ChatGLM, SenseTime etc.'s InternLM and smaller ones. They – well, those groups, not the red boomer Xi – are well aware of their weaknesses and optimize around them (and borrowing from the open academic culture helps, as can be often seen in the training methods section – thanks to MIT&Meta, Microsoft, Princeton et al). They are preparing for the era of machine labor, which for now is sold as means to take care of the aging population and so on (I particularly like the Fourier Intelligence's trajectory, a near-perfect inversion of Iron Man's plot – start with the medical exoskeleton, proceed to make a full humanoid; but there are other humanoids developed in parallel, eg Unitree H1, and they seem competitive with their American equivalents like Tesla Optimus, X1 Neo and so on); in general, they are not being maximally stupid with their chances.
And this, in turn, means that the culture of the next years will be – as I've predicted in Viewpoint Focus 3 years ago – likely dominated by the standoff, leading up to much more bitter economic decoupling and kinetic war; promoting bipartisan jingoism and leaving less space for «culture war» as understood here; on the upside, it'll diminish the salience of progressive campaigns that demoralize the more traditionally minded population.
It'll also presumably mean less focus on «regulation of AI risks» than some would hope for, denying this topic the uncontested succession to the Current Thing №1.
That's about all from me, thoughts?