Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is:
It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years!
Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix.
(Updated post: slightly redacted to not have some personal info)
Luke©️🛡️ retweeted
Introducing Kitesurf: a browser built for agents, running entirely on Cloudflare Workers.
Chromium is too heavy to hand every agent one. Kitesurf is written in Rust, uses 3-7x less CPU and memory, and spins up per request.
Free in beta: blog.cloudflare.com/kitesurf…
Hello, let’s see what’s next!
Introducing Cloudflare Wallets. They will allow you to store stablecoins, purchase services, and receive funds across the web. cfl.re/455An86
Cloudflare 把内部员工天天在用的 AI 工作环境开源了:员工可以让 AI 干活、让 AI 造小应用,权限和风险由系统兜底,别的公司可以整套复制改成自己的版本。
叫 OS 不是传统意义上的操作系统,而是因为两件事:公司怎么用 AI 才安全,以及 AI 工作负载由谁来管。
核心能力有三块:
预载公司知识的智能体聊天界面
沙箱化的应用开发(让 AI 构建"gadget"小应用)
以及名为 Gatekeepers 的能力安全框架。
Today we are releasing Cloudflare OS, a chatbot with connectors, just like every other tech company is doing.
Except actually, it's different. This is a remake of Sandstorm[.]io, my startup from 10 years ago, except this time built on Cloudflare Workers (the platform I've spent the last 9 years building) and deeply leveraging AI. This is more or less the culmination of my secret 10-year master plan.
This is a full-on personal app vibe coding platform, in which the sandbox is so secure that you can pretty much go wild -- the AI cannot introduce a significant security bug. We believe a company's security team can feel comfortable giving non-technical users permission to vibe code and then sleep soundly at night.
How is that possible? It's the Sandstorm security model, revisited. A "Gadget" is the same thing as a Sandstorm "Grain": a fine-grained app instance. For example, if you have a document editor app, each document runs as a separate instance of the app, in a separate sandbox (one "Gadget").
This means two things, both of which I think are Big Deals:
1. The platform can manage all access control, by controlling who can access the Gadget at all. There is no way the Gadget can accidentally leak itself to an attacker -- even an attacker who has access to other Gadgets based on the same app.
2. Since everyone is running their own copy of the code, everyone can freely *modify* their copy of the code.
Think about #2 a bit more.
What if, when you wanted a new feature in the software you are using, you could just prompt your agent to add it?
This doesn't work in the cloud Software-as-a-Service model, because you are not running your own copy of the app.
Sandstorm tried to change that 10 years ago, but the world wasn't ready, because not enough people had the skills or patience to actually modify their software. AI has changed that. Now you just ask the agent -- the same agent that you are using to help you interact with the Gadget can also modify the code of the Gadget.
And it is so fun.
github.com/cloudflare/cloudf…
Claude Code 工程师刚刚放出了一个 28 分钟的实操视频,彻底把我看麻了。
别再去复制那些毫无营养的“AI 提示词模板”了。
这老哥亲自下场,毫无保留的教你如何真正榨取 Claude 200% 的生产力:
从基础的如何配置完美的 CLAUDE.md、到持久化上下文记忆、多线程并行会话,以及那些没人告诉你的高阶 Prompt 逻辑。
我花大价钱买过的那些课,干货密度可能连他头 10 分钟的一半都达不到。
而且最爽的是:全免费。
不管你是刚学编程,还是像我一样整天在终端里切 Tmux 疯狂写全栈项目… 看完这个,你会用一种全新的上帝视角去管理你的 AI 编码工作流。
强烈建议码住。
以后你每次重构项目架构的时候,都会回来翻看无数遍。
Luke©️🛡️ retweeted
黄仁勋前几天接受了Axios的70分钟专访,正面回应了芯片股大跌。信息量很大,我把访谈看完了,划几个重点:
1)半导体要崩盘吗?他说短期不会:这轮是基建驱动不是需求驱动,未来10年半导体行业要扩大5到10倍,现在存储、封装、代工全线短缺。
2)泡沫呢?原话:总有一天会来,但5年内非常不可能。理由很反直觉:电力、土地、工人、芯片全都缺,资本开支想砸都砸不出去,供过于求被不断推迟。"这个约束是好事。"
3)Kimi冲击?市场第二次搞错同一道题——DeepSeek那次英伟达单日跌了17%,这次反应一模一样。免费的AI意味着更多使用、更多算力,反而是硬件的利好。
4)开源会干掉OpenAI和Anthropic吗?恰恰相反,最可能付费的就是用过免费AI的人。他说这两家会是人类历史上最成功的IPO。
5)中国业务?收入约等于零,指引里也没算。中国追没追上不重要——AI不是百米冲刺,中国培养的AI研究员比世界其他地方加起来还多,谁也挡不住谁。
6)Anthropic被锁起来的最强模型Mythos(强到能攻破银行系统),该不该开放?他说应该向所有人开放,加固是Anthropic自己的责任。
7)蒸馏算偷吗?违反条款走法律途径,但AI互相学习是智能的本质——几年后互联网99%的内容都是AI生成的,到时候所有模型都在蒸馏别的模型。
8)机器人的ChatGPT时刻?已经到了(它能推理出放苹果前得先开抽屉),但从有趣到有用,还要3到4年。
我自己的看法:打一个折扣(他是AI领域最大的卖铲人),记一个指标(电力、封装、存储的短缺何时缓解——缓解之日,就是警惕之时)。
详细分析,请见视频。
Luke©️🛡️ retweeted
Anthropic's decline continues.
Following the release of Kimi K3 on July 16, Anthropic’s implied pre-IPO valuation is estimated to have shrunk by approximately 13.1%, representing a decline of about $126.42 billion from its last confirmed valuation and about $531.20 billion from its implied peak of $1,369.78 billion.
Dario is probably worried that if he doesn't get the Trump administration to regulate his open-weight competition now, nothing will be left by the time the company goes public.
Luke©️🛡️ retweeted
Fuc*k your paid courses, @Kimi_Moonshot AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything.
The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS.
This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world.
- Dynamic orchestration, PARL rewards, real scaling beyond context windows.
- Trained with smart RL rewards (instantiation, finish & outcome)
- 100-300+ specialized spawns agents , sub-agents on the fly working in parallel.
- Thousands of tool calls.
- 4.5x faster execution.
This isn't theory it's how they're scaling Kimi to handle real complexity.
Better than most local paid courses.
Swap your Netflix tonight watch this instead.
- be Yang Zhilin the founder of Moonshot AI (Kimi)
- China Tsinghua to U.S CMU PhD (graduated 2019)
- Worked at Google and Meta.
- Papers with 2 Ai-god-fathers LeCun & Bengio
- Co-authored papers on computer reasoning and pattern recognition with Turing Award winners like Yoshua Bengio and Yann LeCun.
- Led the highly-cited XLNet paper over 10k citations, which advanced large language model training.
- Apple wanted him, Stanford/MIT were options.
- Instead, left the US, went back to China in 2019 and founded Moonshot AI (Kimi)
- he’s helping build China’s next AI powerhouse.
Talent like this is rewriting the AI map.
Luke©️🛡️ retweeted
中国开源模型Kimi开始证明,云大厂的资本开支没有必要。美国 AI 资本溢价到顶了?
这是我今天见到最可笑的外行解读和小作文(如同meta和市场算力过剩)。我认为这个消息继续发酵得越厉害,我抄底抄得越安心,谢谢小作文和燃料
从硅谷业内一线 AI 从业人员角度,给大家解释一下我看到的逻辑荒谬点。去年 DeepSeek 事件之后,nvda当天跌了 17%,之后发生什么,一直持续到今年的 Infra 上涨,我就不多赘述了
很多人没有分清推理和训练就开始下结论,或者看大家都在发,通过情绪渲染,洗个稿就立刻发出来,证明自己紧跟 AI 潮流,其实都不知道自己在说什么
推理不是凭空产生的,不是有了模型就能用。模型参数需要托管在 GPU 上,需要用存储kv cache,需要用互联来搬运数据。
模型越便宜、越好用,甚至开源,就越能推动整个行业的进步。这会让更多的人用上好的模型,把蛋糕越做越大,而非在有限的盘子里你多一块、我少一块的零和博弈
当模型更便宜,能让更多的人用得起、更智能,总的推理量会越来越多,还是越来越少呢?社会上是不是有更多的人会用上呢
Kimi 能够训练出来好模型,跟推理 token 的消耗有什么关系?
当然,每个人可能有不同的意见。最关键的是,你作为投资者,你相信它会更多吗?
其次,有一个方法叫“蒸馏”。我对 Kimi 是否蒸馏了sota模型不做评论。我也不觉得训练就到此止步了,模型每三个月就会变得更聪明。
什么时候 Kimi 说,我们不用这么多的算力,能做到赶超拥有强算力的模型而非逼近,我会立刻改变我的想法
但目前 Kimi 自己也承认,跟 GPT 5.6 和 Fable 还是有比较大的差距。
现在吹得最猛的就是这个前端做网页的榜单评分
我看到的更多是情绪渲染和通稿,期待更多的生产力实测
此外现在主流媒体拿着一个大语言模型LLM就认为这是 AGI(通用智能)的终局了,我不这么认为。正如我所说的:端侧 AI、物理 AI 甚至世界模型。
同时,我也再次感谢写小作文的人,希望这个消息继续发酵,能让我们有更好的价格买入
Introducing Kimi K3: Open Frontier Intelligence
🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows
Kimi K3 is now live on on Kimi.com, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.com/blog/kimi-k3
乔帮主对Kimi K3的详细评测报告来了!非常牛逼,但也有一些局限。
6个真实场景项目,每个都能在线体验使用。
模型局限也有几条:
1. 因训练方式原因,与官方的 Kimi Code 匹配更好,如接入 Claude Code,效果可能会有折损。
2. 优化了长程、高难任务,主动性很强;遇到简单、模糊需求,反而会过度思考和发挥。
3. K3 在交互、界面、视觉、空间等相关场景完全可以作为首选模型,但架构、后端等场景,仍落后于最强闭源模型 Claude Fable 5 和 GPT-5.6
Luke©️🛡️ retweeted
Kimi K3 vs GPT-5.6 Sol vs Fable 5
I gave all three models the exact same game design prompt.
I looked at three things: how fast they built it, how good the design looked, and how the gameplay felt.
Results:
Kimi K3 had the best design. It understood what made the game fun and added the right features. The pace and gameplay also felt natural.
Fable 5 and GPT-5.6 Sol both made the gameplay feel too fast. (The video wasn’t sped up.)
Overall ranking (design, features, and cost):
→ Kimi K3: 9.5/10 · $0.030
→ Fable 5: 7.5/10 · $0.38
→ GPT-5.6 Sol: 7/10 · $0.11
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Luke©️🛡️ retweeted
本週的半導體大屠殺讓大家忽略了Jensen 日本行的合作佈局。
黃仁勳提到,日本對這個領域的興趣沉寂了約15至20年,因為當時人形機器人找不到足夠明確的實際用途。
如今情況已經不同。AI開始補上機器人過去最缺乏的感知、推理與適應能力,而日本長期累積的精密製造、機電整合與工業機器人基礎,可能因此重新取得優勢。
對 NVIDIA 而言,最有利的局面不是某一家AI或機器人公司壟斷市場,而是終端市場維持多極競爭,但各陣營都使用 NVIDIA 的運算、模擬、世界模型與機器人開發平台。
無論最後勝出的是美國、中國或日本公司,雲端AI、人形機器人、工業機器人或自駕系統,只要競爭仍建立在 NVIDIA 的技術堆疊上,它就不必預測誰會成為贏家,而是向所有參賽者出售工具。
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黃仁勳在日本召集工業機器人巨頭,要組「機器人復仇者聯盟」?
7 月 16 日,黃仁勳與富士通執行長時田隆仁、發那科執行長山口賢治、安川電機副會長小川昌寬及川崎重工執行長橋本康彥共同現身,宣布展開 Physical AI 的合作研究。
這項合作由富士通主導,與發那科、安川電機及川崎重工三家日本機器人公司,研究如何把輝達的 AI、世界模型、模擬與機器人技術,導入製造、物流及醫療現場。
目前合作仍處於業務與技術研究階段,各公司將先擬定具體的技術開發及商業化路線圖。
#共同建立實體AI控制平台
富士通計畫開發一套連接數位世界與實體設備的「協同控制平台」,整合不同公司的機器人、工業設備、AI 模型、控制系統及企業應用程式。
這套平台將導入輝達的 Cosmos 世界模型、Omniverse 模擬工具、Isaac 機器人開發平台及 Newton 物理引擎,用於建立數位孿生、訓練機器人、進行 Sim-to-Real 測試,以及在正式部署前驗證與最佳化機器人的行為。
富士通也計畫建立共通的軟體平台及硬體介面,讓不同品牌的機器人與設備更容易互相連接,並逐步把這套基礎設施發展為開放平台。
#發那科:讓機器人更容易使用
發那科表示,公司已經開始實際導入以開放平台為基礎的 Physical AI,包括透過語音指令操作機器人,以及讓 AI 代理自主完成零件備料等工作。
在這次合作中,發那科將結合富士通的 AI 基礎設施、Takane 大型語言模型與輝達技術,以及發那科支援 ROS 2、Python 的機器人控制平台。
發那科的目標,是把能夠彈性適應任務、不同技術程度使用者都能操作的 AI 系統帶進工廠,並將機器人、設備、人員及企業應用程式連接起來,涵蓋生產規劃、製造、品質管理及維修。
#安川電機:擴大自主型 AI 機器人
安川電機將以既有的 i³-Mechatronics 自動化架構,以及 MOTOMAN NEXT 自主型 AI 機器人參與合作。
MOTOMAN NEXT 已經將輝達 GPU 列為標準配備,安川也正在推動 ROS 2 相容及機器人平台開放化,讓外部開發者與合作夥伴更容易把 AI 導入機器人。
安川表示,希望透過這次跨公司合作,加速 Physical AI 的實際部署,並把自動化擴展到過去難以透過傳統程式控制的工作。
#川崎重工:從醫院開始導入
川崎重工的合作重點放在醫療與照護領域,計畫把醫院的電子病歷及業務系統,與 AI 和機器人系統連接。
當醫院系統發出任務後,AI 將根據現場狀況制定計畫,再由機器人自主執行藥品與檢體搬運、門診報到、患者引導等工作。
川崎重工也希望逐步建立涵蓋患者入院、看診、治療、手術到術後照護的「醫院一站式解決方案」,讓不同機器人、醫療資訊系統及工作人員可以協同運作。
黃仁勳: #PhysicalAI將在日本製造
黃仁勳將這次合作稱為下一波工業革命。他表示,富士通、發那科、安川電機及川崎重工曾經教會世界如何製造;結合輝達的完整 Physical AI 平台後,下一步是讓機器學會思考、移動,並在工廠、醫院與城市中和人類一起工作。
這次合作並不是成立一家新的機器人公司,而是由富士通建立共通控制平台,三家機器人巨頭分別提供機器人控制技術、設備及現場資料,再由輝達提供世界模型、模擬、機器人學習及運算平台。
#日本政府背書
日本政府已設定2040年前在18個領域部署1,000萬台AI機器人的國家目標,經濟產業大臣赤澤亮正公開表示將全力支持實體AI發展。
黃仁勳表示:「日本對人形機器人失去了興趣長達 15-20 年,因為它並沒有用處。但現在技術已經成熟,它也可能變得有用,所以我真的很希望日本有人能再次創造出世界上最可愛的人形機器人。」
「日本曾教會世界如何製造。現在它有機會重新發明智慧產業時代的製造業。」
Luke©️🛡️ retweeted
原推作者是OpenAI未来战略负责人,他说的话完全是出于OpenAI立场和利益来讲的。
他的观点:
1. Kimi 的实力是真的,不像简单蒸馏出来的。
在智能体编程任务里,Kimi已经接近 2026 年第一季度最强的公开模型。不过它很“吃 token”,实际推理成本未必像宣传中那么便宜。
2. 中国继续开放权重,可能不是因为特别支持开放,而是现实选择。
作者猜测,一方面中国政府没有那么相信 AGI 风险;另一方面受算力限制,中国公司难以长期承担大规模云端推理服务,所以更适合把模型权重放出去,让别人自己部署。对企业来说,开源也是追赶者获取用户和影响力的办法。
3. 开源模型看似“加速”,实际上可能抑制 AI 投资。
模型一旦可以免费下载、复制和部署,商业公司就更难靠模型本身赚钱,资本也会更谨慎地投入下一代模型训练。因此作者认为,开源模型本质上反而具有“减速主义”效果。
4. 开源模型的终点,可能是国家补贴的“AI 公共品”。
如果企业无法靠前沿模型持续盈利,最终可能变成政府出钱建设算力中心、训练模型,再以公共基础设施的形式提供 AI。作者把这种未来称为“AI 共产主义”,并对此非常反感。
5. 美国可能不会直接禁用中国开源模型,而是制造合规风险。
比如监管机构发布安全提示,暗示中国模型可能存在后门、数据泄露或供应链风险。即使证据不充分,只要让银行、医疗、金融等受监管企业感到不安,它们就会主动避开。
6. 当前风险可能有限,但未来能力越强,风险会突然跨过临界点。
今天开放一个强模型,世界可能只是“稍微危险一点”;但当模型具备更强的网络攻击、生物设计或自主复制能力时,开放权重的后果就可能非常明显。(原推还影射了新冠疫情)
我的观点:
OpenAI显然是慌了,开源模型开始削弱单纯靠“卖模型能力”赚钱的空间。对于OpenAI、Anthropic这种前沿模型公司而言,强大的开源模型必定会降低他们的投资回报;但对于创业公司、科研机构和中小公司而言,开源模型能显著提高创新能力。强大的开源模型,让创新重心从少数模型实验室扩散到更广泛的应用和基础设施生态。
曾经浏览器、Linux、安卓都把底层能力免费化,真正赚钱的部分转移到了云服务、硬件、应用、数据和生态。未来基础模型可能也会变成类似操作系统的东西:底座越来越便宜,利润向上层迁移。
Some observations on Kimi:
1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run.
2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China.
3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.
4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business.
5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.
6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
Luke©️🛡️ retweeted
일론머스크가 흥미롭다고 답글 쓴 글
Kimi K3 등장으로
오히려 AI 생태계 전반에 더 좋은 일이 될수 있다는 내용의 글임
알기쉽게 풀어 적어봤음
Kimi K3는 단순히 새로운 오픈웨이트(Open-weight) 모델이 등장했다는 의미를 넘어, AI 산업 구조 자체를 바꿀 수 있는 중요한 변곡점이 될 가능성이 있음
지금까지 AI 시장은 OpenAI, Anthropic, Google과 같은 소수의 프론티어 모델 기업이 최고 성능의 모델을 보유하며 높은 수익률을 가져가는 구조였음
만약 이러한 구조가 계속된다면 모델 기업은 AI 생태계에서 가장 큰 협상력을 갖게 되고, 전력, 데이터센터, 반도체, 하이퍼스케일러는
물론 장기적으로는 소프트웨어(Application) 영역까지 수직계열화 할 가능성이 높음
결국 AI 산업의 부가가치 대부분이 소수의 모델 기업으로 집중되는 구조가 만들어질 수 있다는 거임
Kimi K3와 같은 오픈웨이트 모델은 이러한 구조를 흔들 수 있는 변수임
오픈웨이트 모델은 기업이 직접 모델을 내려받아 자체 인프라에서 운영할 수 있기 때문에 특정 API 사업자에 대한 의존도를 낮출 수 있음
이는 모델 계층의 경쟁을 촉진하고 높은 마진을 압박하는 효과를 가져옴
여기서 중요한 점은 오픈웨이트 모델이 등장한다고 해서 GPU 사용량이 줄어드는 것은 아니라는 것임
동일한 규모와 구조의 모델이라면 오픈 모델과 폐쇄형 모델 모두 비슷한 수준의 연산량이 필요함
즉 OpenAI API를 사용하는 대신 기업이 직접 GPU를 구매해 모델을 운영할 뿐이며, GPU와 메모리, 데이터센터에 대한 수요 자체는 크게 달라지지 않응
이러한 이유로 엔비디아의 젠슨 황이 오픈 모델을 긍정적으로 바라보는 것으로 해석할 수 있음
다만 Kimi K3가 아직 완전한 게임체인저라고 보기는 어려움
이유는 토큰 효율이 경쟁 모델보다 떨어진다는 평가를 받고 있기 때문임
AI 모델의 경제성은 단순히 토큰당 가격만으로 결정되지 않음
실제 업무를 완료하기 위해 얼마나 많은 토큰을 소비하는지가 훨씬 중요함
만약 동일한 작업을 수행하는 데 GPT-5.6이 100개의 토큰을 사용하는 반면 Kimi K3가 180개의 토큰을 사용한다면, 토큰당 가격이 같더라도 실제 비용은 훨씬 높아진다는 거임
결국 중요한 지표는 토큰당 가격이 아니라 같은 비용으로 얼마나 높은 수준의 지능을 제공하는가, 즉 달러당 지능
이러한 관점에서 장기적인 승자는 가장 저렴한 모델이 아니라 가장 높은 비용 대비 성능을 제공하는 모델이 될 가능성이 높음
모델 계층의 마진이 낮아질수록 AI 생태계의 다른 계층은 오히려 더 많은 수혜를 받을 수 있음
기업들이 자체적으로 모델을 운영하게 되면 GPU, 고대역폭 메모리(HBM), 데이터센터, 클라우드, 전력 인프라에 대한 투자가 확대되고, AI 애플리케이션과 소프트웨어 기업들도 특정 모델 사업자에 종속되지 않고 경쟁할 수 있는 환경이 조성됨
즉 모델 기업의 마진 감소는 인프라와 소프트웨어 계층의 성장으로 이어질 가능성이 있다는 거임
물론 이것이 곧바로 OpenAI와 Anthropic에 부정적인 결과를 의미하는 것은 아님
첫째, 사용자들은 단순히 모델 성능이 아니라 ChatGPT와 Claude가 제공하는 제품경험과 생태계를 선택할 수도 있음
둘째, OpenAI와 Anthropic이 아직 공개하지 않은 차세대 모델을 내부적으로 개발하고 있을 가능성도 존재함
만약 이들이 재귀적 자기개선(Recursive Self-Improvement, RSI)을 경쟁사보다 먼저 달성한다면 몇 달의 기술 격차만으로도 장기적인 우위를 확보할 수 있다는 시나리오도 충분히 가능하다는 거임
결국 Kimi K3의 진정한 의미는 모델 하나의 성공 여부가 아님
프론티어 수준의 오픈웨이트 모델이 등장했다는 사실 자체가 AI 산업의 경쟁 구도를 바꾸기 시작했다는 점에 있음
다만 현재 Kimi K3는 토큰 효율 측면에서 아직 한계를 보이고 있으며, OpenAI와 Anthropic의 높은 수익 구조를 근본적으로 흔들기 위해서는 성능뿐 아니라 토큰 효율까지 경쟁력을 갖춘 오픈웨이트 모델이 추가로 등장해야 할 가능성이 높음
앞으로 AI 산업의 승부는 누가 가장 비싼 모델을 판매하는지가 아니라, 누가 가장 낮은 비용으로 가장 높은 수준의 지능을 제공하는지,
그리고 그 과정에서 AI 생태계 전체의 부가가치를 어떻게 분배하는지가 핵심 경쟁력이 될 것임
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.
Rationale:
A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.
Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.
This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.
Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.
Time will tell on both points. And likely fairly quickly.
Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
這幾天在推特上針對 Kimi K3 的討論熱鬧滾滾,不知道為什麼,體感上我的臉書牆上討論蠻少的,可能被世足跟股市的恐慌感蓋過。
中國的月之暗面(Moonshot AI)剛發布 Kimi K3,在 Artificial Analysis 的智慧指數衝上全球第三,跟 Claude Opus 4.8、GPT 5.5 同一個等級,還宣布會開放完整權重。一個免費模型追到閉源前沿的腳邊,市場同時冒出兩種恐慌,一種怕它拖垮 OpenAI 和 Anthropic,另一種怕它的新架構害死 NVIDIA 和記憶體廠。過去 24 小時,投資圈和半導體研究圈各有一篇指標性分析出手回應,兩篇的答案都跟恐慌的方向相反。
第一篇來自 Atreides Management 創辦人 Gavin Baker,他認為 Kimi K3 對 Anthropic 和 OpenAI 潛在負面,對其他幾乎所有公司都是淨利多。推論是這樣的,如果前沿模型市場最後只剩兩三家實驗室,坐擁 90% 的推論毛利,它們會變成電力、資料中心、半導體的獨買方,再垂直整合吃掉每一層。任何壓低模型層毛利、增加競爭的力量,對電力、半導體、雲端業者甚至軟體業都是好事,這也是黃仁勳一直力挺開源的原因。馬斯克(Elon Musk)在這則貼文底下留了一句「有意思」,拿下 900 多個讚。
不過 Baker 補了一個保留,K3 是個詞元(token)大胃王,依他引用的 Artificial Analysis 數據,它每個詞元的單價跟 GPT 5.6 差不多,同樣的任務卻要多花 50% 到 70% 的成本。真正的「史普尼克時刻」(Sputnik moment),要等一個既開源、詞元效率又高的前沿模型出現才算數。
第二篇來自 SemiAnalysis,回應的是硬體圈的擔憂。K3 採用線性注意力架構 KDA(Kimi Delta Attention),KV 快取的網路傳輸需求最多只有傳統架構的十分之一,有人因此推論 GPU、HBM、DRAM 和網通的需求會跟著縮水。SemiAnalysis 發了八則推文,從硬體規格逐條反駁。
K3 的參數規模達 2.8 兆,依 SemiAnalysis 估算,光是權重就吃掉超過 1.5 TB 的 HBM 容量,KV 快取反而得外移到 CPU 記憶體和 SSD。它的 896 個專家模組透過 WideEP 技術打散到大量 GPU 上,這個優化極度消耗網路頻寬,剛好是 NVIDIA 機櫃級系統 GB200 NVL72 的主場,銅背板頻寬是一般 DGX B200 的 18 倍。連 Kimi 官方都說,K3 要跑得順,至少需要 64 顆晶片互連的機櫃。SemiAnalysis 最後搬出傑文斯悖論(Jevons Paradox)收尾,注意力機制越有效率,AI 就用得越廣,GPU、記憶體和網通的總需求只會往上走。
兩篇合起來讀,指向同一件事。開源前沿模型壓的是模型層的利潤,壓不到賣鏟子的人;下游多一個即將開放權重的頂級模型,上游就多一排新客人。該緊張的,從頭到尾只有坐在 90% 推論毛利上的那兩三家實驗室。
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>be Chang Liu
>senior system electrical engineer at Apple
>8 years working on iphone
>january 2026: leave Apple to join OpenAI
>apple asks for laptop back
>ignore them
>lmao it’s my laptop now
>within HOURS of leaving
>message Yu-Ting “Alyssa” Peng, friend at Apple:
Liu: “I still have another computer”
>uses it to access Apple secret info
>within weeks, use HER Apple work laptop
>february 9: try Apple’s network storage
>cloud repo of confidential engineering files
>authentication bug. still works!
>message Peng: “LOL, I found out I can access the [network storage], so funny”
Peng: “I’m ready”
>while developing hardware for OpenAI
>download DOZENS of confidential files
>including a thousand-plus-page compilation of technical files
>including MLB (main logic board) manufacturing + testing presentations
>send Peng links to Apple’s proprietary folders
>point her to specific project data
>coach her how to copy files “to avoid trouble with the security team”
>tell her which confidential Apple materials to study before her OpenAI interview
>warn her another guy “fumbled” Tang Tan’s questions about a secret Apple project
>“download some info” for her to review
>tell her: switch to LINE Messenger so nobody sees this
>she gets the OpenAI offer, leaves Apple April 16
>meanwhile every message was left on APPLE-ISSUED WORK LAPTOPS
>july 10: Apple Inc. v. Chang Liu
>named first. before OpenAI. before Tang Tan
LOL
so funny








































