Notes on DeepSeek(twitter.com) |
Notes on DeepSeek(twitter.com) |
I assume it will get reposted at some point.
Notes on DeepSeek:
We visited the company HQ last Tuesday. It was founded in 2023 by Liang Wenfeng and operated out of his hedge fund, High-Flyer, until somewhat recently. The company released their R1 model in January 2025, so it was interesting to see what they’ve been doing
The company is located in an unmarked, 12-story building in Hangzhou. There is no DeepSeek branding visible from the street or lobby. I asked why this is, and the team demurred and said, “Well, there are many companies in this building, and we are not special.” They want to keep a low profile.
We met with their Head of Data and Head of Infrastructure. The company only has 300 employees. They are at least an order-of-magnitude smaller than Anthropic, and don’t care to scale further just yet. Their Head of Infrastructure, in particular, was young; maybe 30 years old and apparently one of the best AI buildout and energy experts in the country. (We briefly walked through the labs, and everybody seemed young. There was a lot of discussion; it felt like an exciting and energetic place.)
Lots of competition is coming from Alibaba (Qwen), ByteDance, and Moonshot (Kimi). People in China seem to mostly use Kimi or Deepseek. Young people use VPNs to access Claude, though Anthropic has blockers around usage in China and make it difficult. Poaching between groups is common, just like in the U.S. DeepSeek has a reputation as being really smart and “cool,” maybe similar to Anthropic. Big labs are mostly in Beijing, near Tsinghua and Peking University, with Hangzhou as the main exception (DeepSeek and Alibaba/Qwen are there).
The DeepSeek team reads western AI writers. They listen to Dwarkesh and read Gwern. The people we met with said they had never met with any employees from Anthropic. They were not at all concerned with some kind of hostile / AGI takeover scenario. They kept bringing up job loss (which is already high amongst youth in China) as their main concern. When we asked if they do red teaming on their models, they said no. In China, AI models are not regulated directly; the government instead has restrictions on how those models can be used in software, services, etc.
As a whole, China seems to treat AI as just another technology, rather than as some kind of singularity moment. National attention is still on basic needs and infrastructure buildouts, and on providing more medicines for people. The “dreams of singularity" seem like a luxury or distant consideration.
We asked the DeepSeek team: “What has the highlight been so far? What are your plans for an exit?” And they said that their highlight and great achievement was R1. They did not gesticulate at a future model or vision, but rather seemed proudest of what they’ve already done. They are content for now to remain ~6 months behind U.S. companies while maintaining a lower profile and team size.
oh jesus, that guy and his absolute baloney, empty interviews.... sigh.
We're lucky to have China imposing competiton to the western AI megacorps.
If it wasn't for China, I would probably have to spend $100/mo on AI instead of $10 like I do currently while using DeepSeek and MiMo (opencode Go plan).
And while I could do so comfortably, I feel for those who can't. It must feel incredibly isolating to only watch others have access to expensive models to leverage their careers.
I hope SoTA AI becomes an universal right because it will contribute to too much income disparity otherwise.
The second they get a hold of the market, Chinese Big Tech will be as bad or worse than US Big Tech.
We're lucky to have DeepSeek.
I have unlimited tokens at work than i go home what do i do? Spend 200$ per month? No def not.
When Anthropic increased the limits for their 20$ plan, i started again coding with it on a private project and it was fun and i did a lot in that 4 weeks.
We've had a taste, and damned if I'm going to have the "means of production" snatched from me already?
This is a refreshing perspective.
> It got me thinking, though--the most successful founders do not set out to create companies. They are on a mission to create something closer to a religion, and at some point it turns out that forming a company is the easiest way to do so. [1]
From the notes this part sat with me as the real difference:
> As a whole, China seems to treat AI as just another technology, rather than as some kind of singularity moment. National attention is still on basic needs and infrastructure buildouts, and on providing more medicines for people. The “dreams of singularity" seem like a luxury or distant consideration.
Meanwhile... In the fantasy land over here in the US we're constantly being told that it's "coming", "almost here", "too powerful for us to give you access to", "of national security importance!". Or... FUD.
And while there may be trace amounts of truth in those overzealous statements we haven't seen a significant improvement in much outside of software development comparative to the spend and environmental impact.
Expert in buildout or expert in distillation?
[0] https://www.reuters.com/world/china/china-prepares-295-billi...
[1] https://www.globalneighbours.org/en/articles/china-unveils-n...
[2] https://english.www.gov.cn/news/202606/10/content_WS6a296017...
in the early 2000s in california universities you'd get marked down for citing wikipedia. so the good souls told everyone "see the number in brackets[2] after what you're trying to cite the article for? just click that then click the archive.org or whatever link there, then cite that."
Now? i think wiki is considered a valid source? or has it flopped back to being "unreliable"?
[0]: https://en.wikipedia.org/wiki/Wikipedia:Citing_Wikipedia
Just like how people should use AI for research, I guess.
I don't have enough information to say whether the Chinese leadership sees AI "just as the next technology" or they are more cautious due to its double-sword nature. But the immense efforts for building their own AI/GPU chips plus government's billions fund pushed for AI build out, a directive for fast pace integration on large scale and a sweeping national education reform for AI, I don't think it can be seen as similar to other ordinary techs.
[0] https://www.reuters.com/world/china/china-prepares-295-billi...
[1] https://www.globalneighbours.org/en/articles/china-unveils-n...
[2] https://english.www.gov.cn/news/202606/10/content_WS6a296017...
Further on. Refreshing indeed.
I hypothesize that, rather than slowly having it disperse in society and allow people to harness it in ways they don't want, they might as well accelerate everything until AI becomes the totalitarian swiss knife - which they can make use of in the best way of course.
Let's see what will happen.
[0] https://www.washingtonpost.com/national-security/2026/03/11/...
It's trivial for me to download one of their models and run it on my Spark, and there's all sorts of ways to strip out their Tiananmen-denialism or whatever.
If/when the memory price crunch dissipates, even more so. And so far it's only China I see as making moves to increase production capacity on memory, too.
If anything the centralization of capital into US-based Anthropic and OpenAI is far more terrifying from the perspective you're outlining.
The issue is that Wikipedia can be wrong and you’d only know that by going to the source (or lack thereof), or checking other sources.
It's a smart move to make everyone dependent on them.
1) China distills and is therefore morally bad.
As you rightly point out, that's not a great argument.
2) China distills and is therefore possibly not that competent.
I think that makes sense. If they only catch up to the frontier through distillation then 1) Their model will never be as good as the model they are distilling from. 2) They will never reach the frontier - they need someone else to do it first.
“All they do is copy.”
And now, oops they are world leaders in EVs, batteries, solar, drones, just to name a few on the biggest consumer facing things.
You gotta start somewhere and you can start at page 1 or page 10 and that time, energy and cost you saved starting 9 pages later can be put into making whatever it is you're building better than the original.
The US, and every other country, is full of derivatives or straight up copies. No one is getting super mad at the generic cheerios at the grocery store. It's hypocrisy.
I think deepseek at least has done enough innovative work that you could grant them a baseline of competency.
In general, there are enough papers coming out of China to suggest that there are quite a few people there who know what they are doing.
I also have a soft spot for deepseek because they write such readable papers. I don't have a degree in anything but with a little work I can understand their papers - which I really appreciate.
But I still think my point stands - if you need distillation you won't be SOTA
I heard that argument more than one year ago, when chain of thought and reasoning cycles started to be hudden to protect against distillation.
Meanwhile, models as DeepSeek and MiMo are nothing short of excellent nowadays.
Ever since I switched away from OpenAI to DeepSeek I never felt the need to go back.
Together it gives me plenty of head room/model performance for $40ish/mo, plus letting me compare the various models over time.
Originally I'd been using the Z.AI plan (that I'm still grandfathered into for <1 yr) as my cheap plan but wasn't keeping up with the SOTA progress and is slow/limited now. So I subscribed to the Opencode Go plan and use Deepseek Flash V4 almost exclusively and it is insane how much usage I can get for $10/mo.
I did the math on my Flash usage vs. what I'm paying Opencode and I'm typically not even exceeding $10 in API costs! So it's actually sustainable not rugpull pricing at least for me. I can pound it with requests/agentic loops and have it running for 30 min doing whatever the fuck and check back and have spent literal pennies for what would have cost $30+ on my work's Github Copilot plan.
I know enterprise world works under different rules and isn't price sensitive in the same ways as an individual but I truly don't see how this is sustainable for the US AI giants in the long term to maintain like 25x+ markup for 1.25x performance benefit.
IMO it does help explain the recent emphasis on secret, scary "super models" like Mythos to muddy the waters for decision makers with hype and FOMO at at time when companies are beginning to seriously scrutinize their token spending for the first time.