Karpathy最新开喷:一句话让全场Agent开发者安静了

marsbitPublished on 2026-07-06Last updated on 2026-07-06

Abstract

安德烈·卡帕西在近期分享中对当前AI Agent热潮提出尖锐批评。他认为,行业最大的错误是急于让Agent执行复杂任务,却忽视了底层大模型的基础研究。卡帕西以自己2016年在OpenAI的“World of Bits”项目失败为例,指出当时因技术(强化学习)不成熟而受阻,而如今成功的路径已完全不同。 他给出三点核心建议:第一,应优先夯实底层模型能力,而非追逐Agent应用;第二,做出炫酷Demo很容易,但将其转化为可靠产品可能需要长达十年的工程努力,自动驾驶和VR即是前车之鉴;第三,Agent本身并非产品,强大的基础能力才是,地基牢固后,高级能力会自然涌现。 同时,卡帕西认为开发者应更多从神经科学(如海马体、基底神经节的工作原理)中汲取构建智能的灵感。他最后指出,在Agent这一新兴领域,最前沿的探索者并非OpenAI等巨头,而是广大的独立开发者和创业者。因为大厂在基础模型上积累了深厚经验,但在Agent的实用化探索上,大家几乎处于同一起跑线,小团队的灵活与快速试错可能更具优势。 卡帕西的核心观点是反对浮躁,呼吁回归技术本质,在扎实的基础上进行长期建设。

Karpathy内部炸场:逼Agent干活是AI最大错误!最前沿不在OpenAI,在你手里。

一句话,把整个Agent圈子浇了个透心凉。

Andrej Karpathy——现Anthropic预训练团队核心研究员,最近在一场面向Agent开发者的现场分享里,甩出一句让全场安静的暴论:

当前AI领域最大的错误,就是人们急着逼Agent干活,却根本没先把底层的大模型搞明白。

这段视频被剪出来扔到X上,几天就传疯了。

因为它戳中的,恰恰是眼下最热、最挤、所有人都往里冲的那条赛道。

而说这话的人,不是外行泼冷水,是踩过坑的人在复盘自己的血泪。

真金白银烧出来的教训

把时间拨回到2016年。

那时Karpathy在OpenAI搞一个项目,叫World of Bits,目标听起来特别「2026」:让Agent学会用键盘鼠标操作电脑,去订机票、点外卖,帮你把活干了。

熟不熟悉?这几乎就是今天所有Agent创业公司PPT第一页的画面。

结果呢?没做成。

Karpathy说得很直白:当时他和Tianlin Shi、Jim Fan几个人一起干,对着几个简陋网页疯狂点鼠标、试图订张机票、点份吃的,最后还真在ICML 2017发了篇论文。

论文标题就叫《World of Bits: An Open-Domain Platform for Web-Based Agents》——一个关于「比特世界」的宏大构想,最终困死在了几个简陋网页上。

技术没准备好。手里唯一的锤子是强化学习,怎么使劲都砸不出来。

回头看,那时候真正正确的做法,是彻底忘掉Agent,转头去做语言模型。

五年后,工具箱彻底换了——你们现在做Agent,几乎没人再用强化学习了。这在当年,根本无法想象。

有意思的是,当年和他一起写论文的Jim Fan,如今已是NVIDIA的高级研究科学家,搞出了Voyager、MineDojo等一系列炸裂项目,在NeurIPS拿了杰出论文奖。

一个2016年的「失败项目」里的年轻实习生,十年后成了AI Agent领域的顶级玩家。

但走的路,不是2016年那条。

Demo很容易,产品要花十年

顺着这个教训,Karpathy给了三步忠告,句句反着当下的热潮来。

第一步,别再逼你的Agent什么都干,先把底层模型做对。

今年5月他加入Anthropic预训练团队时,在X上写的第一句话就是:我认为接下来几年LLM前沿的工作将尤为关键。

一个「发明」了vibe coding、让Collins辞典把它评为年度词汇的人,此刻选择回到最底层的预训练研究——这本身就是对Agent热潮的一记「行为投票」。

第二步,Demo很容易,把它做成产品要花十年。

他搬出两个所有人都熟的例子:自动驾驶,让一辆车绕着街区跑一圈的Demo谁都能做,可真做成产品,用了整整十年,他自己在Tesla就亲历了这场马拉松。

VR也一样,惊艳的Demo满天飞,落地成产品同样是十年起步。

Agent,就是这一类。

极容易想象、极容易做Demo,却极难做成真正的产品。

你要真入这行,就得准备好干十年,而不是做完一个炫酷Demo就以为上岸了。

第三步,Agent不是产品,基础能力才是产品。把地基打牢,Agent会自然涌现出来。

这三句话,几乎把当下「套个壳、堆个Agent、赶紧发布」的玩法,做了彻头彻尾的否定。

Karpathy的意思很清楚,地基不牢,楼盖得越快,塌得越狠。

自动驾驶已经用十年替所有人验证过一遍了,Agent没有理由能跳过这一课。

向大脑偷师

说完教训,Karpathy话锋一转,一头扎进神经科学找灵感。

他在台上抛出一连串问题:Agent里什么东西相当于海马体,负责记忆、索引和检索?

什么相当于基底神经节,控制行为选择和动作执行?什么相当于丘脑,那个「多个念头抢麦克风」、像意识之座一样的地方?

一个顶级AI研究者在说:我们造数字生命,眼下最缺的不是更花哨的功能,而是对「智能到底是什么」这个根问题的敬畏。

他甚至专门带了一本David Eagleman的神经科学著作《Brain and Behavior: A Cognitive Neuroscience Perspective》推荐给在场所有人。

在他看来,今天造Agent,值得像深度学习早期那样——当年我们从单个神经元的结构里偷来了人工神经网络的灵感,如今完全可以再去大脑里偷一次。

真正炸场的,是最后这句

如果说前面是泼冷水,Karpathy的结尾,又给台下点了一把火。

他对着满屋子独立开发者和创业者说:

真正站在Agent能力最前沿的,是你们。不是OpenAI,不是DeepMind,是你们。

这不是场面上的客套。他给了一个特别扎心的解释:

像OpenAI这样的大厂,训练大规模Transformer语言模型的确无人能及——一篇新的Transformer训练论文出来,内部Slack里的反应往往是「哦,这个两年半前有人试过了,为什么没成,我们门儿清」。

可一旦一篇新的Agent论文冒出来,所有人的反应却是:「哦,这真酷,真新颖。」

为什么?因为在Agent这件事上,没有任何一家大厂积累了五年。

大厂并不站在能力的边缘,而你们——创业者、黑客——才站在那条边缘上。

道理其实不难懂。

大厂在语言模型这条路上跑了这么多年,早把每一个坑都踩遍、每一条弯路都标好;可Agent是一片刚被开垦的新大陆,谁都没有五年的先发家底,大家几乎站在同一条起跑线上。

这时候,灵活、敢试、能快速调头的独立开发者,反而比船大难掉头的巨头更有机会撞出新东西。

回到那句最初的暴论

Karpathy要泼的冷水,不是「别做Agent」,而是「别跳过基础去做Agent」。

他自己就是最好的注脚——这位发明了vibe coding、把Agent用到飞起的人,在2026年最重要的职业选择却是:回到预训练,回到大模型最底层的那间实验室。

他要点的火,也不是让人焦虑,而是告诉每一个正在一线折腾的人:这场仗,你并不落后,你就在最前面。

热潮总会退去,Demo也终会褪色。

但把底层模型吃透、愿意为一件事扎进去十年的人,才配站到十年之后的岸上。

参考资料:https://x.com/0xCodila/status/2073544407643496771

本文来自微信公众号“新智元”,作者:ASI启示录,编辑:所罗门

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Related Questions

QKarpathy认为当前AI领域最大的错误是什么?

A他认为最大的错误是人们急着逼Agent干活,却根本没先把底层的大模型搞明白。

QKarpathy以自己2016年在OpenAI的哪个项目为例,说明了早期Agent尝试的失败?

A他以自己参与的“World of Bits”项目为例,说明当时试图让Agent用键盘鼠标操作电脑来完成订票等任务,但因技术未准备好(主要依赖强化学习)而失败。

QKarpathy对Agent开发者提出了哪三步忠告?

A第一,别急着让Agent什么都干,先做好底层模型;第二,Agent做Demo很容易,但要成为真正的产品可能需要长达十年的时间;第三,Agent本身不是产品,基础能力才是产品,打好地基Agent会自然涌现。

QKarpathy为什么建议开发者向神经科学寻找灵感?

A他认为当前造Agent最缺的是对“智能本质”的深刻理解,就像深度学习早期从神经元结构获得灵感一样,现在可以借鉴大脑中如海马体(记忆)、基底神经节(行为控制)等部位的功能来设计更好的Agent架构。

Q为什么Karpathy说Agent能力的最前沿不是OpenAI等大厂,而是独立开发者和创业者?

A因为在大模型预训练方面,大厂有多年积累,踩遍了坑;但在Agent这个新兴领域,没有人拥有五年的先发优势。独立开发者和创业者更灵活、敢试错、能快速迭代,因此更有可能探索出前沿的新能力。

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The project showcases the innovative spirit of the crypto industry, inviting further exploration into its myriad possibilities. While the journey of SPERO,$$s$ is still unfolding, its foundational principles may indeed influence the future of how we interact with technology, finance, and each other in interconnected digital ecosystems.

93 Total ViewsPublished 2024.12.17Updated 2024.12.17

What is $S$

What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

763 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

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