Sam Altman

Sora更新 #1 || Sora update #1

2025-10-04T14:35:29+00:00

我们正从用户对Sora的使用方式中快速学习，并积极采纳用户、版权方及其他相关群体的反馈。当然，在发布前我们已进行了大量讨论，但如今产品已上线，我们不仅能停留在理论层面。

我们即将推出两项调整（未来还会有更多）。

首先，我们将为版权方提供更精细的角色生成控制权，类似于肖像权的选择加入模式，但会附加更多调控选项。

许多版权方向我们表达了他们对这种新型"互动式同人创作"的热情，认为这种互动形式将为他们创造巨大价值，但他们希望能具体规定角色如何使用（包括完全禁止）。我们预计不同版权方会尝试截然不同的策略，并找到最适合自己的方式。但我们将对所有人采用统一标准，由版权方自行决定如何参与（当然我们的目标是让这种模式足够吸引人，促使多数人愿意加入）。可能会有个别不应通过的生成内容漏网，完善我们的系统需要多次迭代。

特别要提及的是，日本创作者们的惊人产出令我们印象深刻——用户与日本内容之间的深厚联结实在令人惊叹！

其次，我们必须为视频生成找到可持续的盈利模式。目前每位用户生成的视频量远超预期，且大量视频的受众群体非常小众。我们计划尝试与允许用户生成其角色的版权方共享部分收益。具体模式需要经过试验调整，但我们打算尽快启动。我们希望这种新型互动形式的价值能超越收益分成，当然两者都具备价值才是理想状态。

请做好我们会高频更新的准备；这让我想起ChatGPT的早期阶段。我们会做出一些正确决策，也会有些失误，但我们将迅速收集反馈并纠正错误。我们计划先在Sora上试验不同方案，然后将成熟经验应用到所有产品中。

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We have been learning quickly from how people are using Sora and taking feedback from users, rightsholders, and other interested groups. We of course spent a lot of time discussing this before launch, but now that we have a product out we can do more than just theorize.

We are going to make two changes soon (and many more to come).

First, we will give rightsholders more granular control over generation of characters, similar to the opt-in model for likeness but with additional controls.

We are hearing from a lot of rightsholders who are very excited for this new kind of "interactive fan fiction" and think this new kind of engagement will accrue a lot of value to them, but want the ability to specify how their characters can be used (including not at all). We assume different people will try very different approaches and will figure out what works for them. But we want to apply the same standard towards everyone, and let rightsholders decide how to proceed (our aim of course is to make it so compelling that many people want to). There may be some edge cases of generations that get through that shouldn't, and getting our stack to work well will take some iteration.

In particular, we'd like to acknowledge the remarkable creative output of Japan--we are struck by how deep the connection between users and Japanese content is!

Second, we are going to have to somehow make money for video generation. People are generating much more than we expected per user, and a lot of videos are being generated for very small audiences. We are going to try sharing some of this revenue with rightsholders who want their characters generated by users. The exact model will take some trial and error to figure out, but we plan to start very soon. Our hope is that the new kind of engagement is even more valuable than the revenue share, but of course we we want both to be valuable.

Please expect a very high rate of change from us; it reminds me of the early days of ChatGPT. We will make some good decisions and some missteps, but we will take feedback and try to fix the missteps very quickly. We plan to do our iteration on different approaches in Sora, but then apply it consistently across our products.

索拉2 || Sora 2

2025-10-01T16:13:09+00:00

# Sora：一款创意视频应用的全新启航

我们即将推出名为**Sora**的新应用。它融合了新一代模型**Sora 2**与创新产品功能，让视频创作、分享和观看变得前所未有的简单。对我们许多人而言，这仿佛是"创意界的ChatGPT时刻"——充满新鲜趣味。从灵感到成果的极速转化，以及由此衍生的新型社交互动，都令人振奋。创意领域可能即将迎来"寒武纪大爆发"，艺术与娱乐的质量也将飞跃提升。

## 核心亮点
- **开放创意场域**：早期测试中，Sora展现的创作自由度令人惊艳
- **角色一致性技术**：团队精心打造的"客串功能"可让你和朋友自然融入视频，成为测试中最受欢迎的连接方式
- **社交新维度**：创造了一种出人意料的沉浸式互动体验

## 责任与挑战
我们清醒认识到：
- 此类服务可能引发成瘾问题
- 存在被用于网络霸凌的风险
- 需警惕AI视频生成的滥用导致用户陷入算法优化的低质内容漩涡

团队已采取多重防护措施：
- 防止深度伪造滥用
- 屏蔽不良/非法内容
- 定期评估用户心理影响
- 持续探索更完善的解决方案

## 产品原则
1. **长期用户价值优先**  
   - 核心指标：用户回顾6个月使用经历时，应感觉生活因Sora变得更美好
   - 否则将进行重大调整（必要时终止服务）

2. **用户主导内容消费**  
   - 支持按需求定制内容（放松/激励/特定兴趣）
   - 未来支持自然语言精准控制
   - 青少年模式含非个性化推送/私信关闭等功能

3. **创作普惠化**  
   - 降低创作门槛，激发人人参与的成就感
   - 坚信创造是人类天性，关乎根本幸福

4. **助力长期目标实现**  
   - 深度理解用户真实需求（社交/健康/事业等）
   - 提供对应内容支持
   - 尊重用户自主选择权（包括"愤怒刷屏"等行为）

我们期待Sora能放大科技带来的美好，同时谨慎规避潜在风险。这款产品将随着用户反馈持续进化。

（注：采用Markdown格式呈现，通过分级标题突出核心模块，关键信息使用列表和强调格式，在保持原文要义的基础上进行了符合中文阅读习惯的段落重组）

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我们即将推出一款名为Sora的新应用。它融合了全新模型Sora 2的技术优势，是一款能轻松创作、分享与观看视频的创新产品。

对我们许多人而言，这仿佛是"创意领域的ChatGPT时刻"，充满新鲜趣味。从灵感到成果的极速转化，以及由此衍生的新型社交互动模式，都令人振奋。

创意领域可能即将迎来寒武纪式大爆发，艺术与娱乐的品质将随之飞跃。即便在Sora的早期测试阶段，其开放包容的创作环境已让众多参与者惊叹不已。

特别值得一提的是角色一致性功能——团队倾注心血开发的"客串特效"，让我们在测试中乐此不疲。这种将自身与好友融入视频的新颖方式，已成为许多人眼中极具吸引力的社交纽带。

我们亦心怀敬畏。社交媒体对世界的影响向来利弊参半。我们深知此类服务的潜在成瘾性，也预见到其可能被滥用于网络霸凌的多种情形。

不难想象AI视频生成的极端场景：人类沉溺于算法优化的信息泥潭。团队精心设计产品机制以避免此类陷阱，并已提出多项可行性方案。产品初期我们将尝试不同路径。

除现有防护措施（包括防止深度伪造滥用肖像、屏蔽不良非法内容、定期评估用户心理影响等）外，我们确信若Sora大获成功，仍需持续完善。以下是指导产品发展的核心原则：

*以长期用户满意度为核心。多数用户在回溯半年使用体验时，应感受到Sora提升了生活品质。若非如此，我们将大刀阔斧改革（若无法改善则会终止服务）。

*赋予用户内容控制权。你可以明确告知Sora需求——想观看舒缓还是振奋的视频？特定兴趣内容？或限定使用时长？随着技术进步，最终你将能用自然语言细致定制。（青少年家长控制功能包含关闭个性化推荐及私信等选项）

*创作体验优先。我们致力于让每个人都能轻松享受创作乐趣；相信人类天生具有创造力，而创造正是幸福之源。

*助力用户实现长期目标。我们愿洞悉用户真实诉求并助其达成。无论是增进友情、健身塑形还是创业逐梦，Sora都将提供对应支持。即便你只想沉浸式刷屏宣泄情绪，我们同样予以尊重（虽然希望用户认为时间花得值，但我们不会家长式定义何为"值得"）。

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We are launching a new app called Sora. This is a combination of a new model called Sora 2, and a new product that makes it easy to create, share, and view videos.

This feels to many of us like the “ChatGPT for creativity” moment, and it feels fun and new. There is something great about making it really easy and fast to go from idea to result, and the new social dynamics that emerge.

Creativity could be about to go through a Cambrian explosion, and along with it, the quality of art and entertainment can drastically increase. Even in the very early days of playing with Sora, it’s been striking to many of us how open the playing field suddenly feels.

In particular, the ability to put yourself and your friends into a video—the team worked very hard on character consistency—with the cameo feature is something we have really enjoyed during testing, and is to many of us a surprisingly compelling new way to connect.

We also feel some trepidation. Social media has had some good effects on the world, but it’s also had some bad ones. We are aware of how addictive a service like this could become, and we can imagine many ways it could be used for bullying.

It is easy to imagine the degenerate case of AI video generation that ends up with us all being sucked into an RL-optimized slop feed. The team has put great care and thought into trying to figure out how to make a delightful product that doesn’t fall into that trap, and has come up with a number of promising ideas. We will experiment in the early days of the product with different approaches.

In addition to the mitigations we have already put in place (which include things like mitigations to prevent someone from misusing someone’s likeness in deepfakes, safeguards for disturbing or illegal content, periodic checks on how Sora is impacting users’ mood and wellbeing, and more) we are sure we will discover new things we need to do if Sora becomes very successful. To help guide us towards more of the good and less of the bad, here are some principles we have for this product:

*Optimize for long-term user satisfaction. The majority of users, looking back on the past 6 months, should feel that their life is better for using Sora that it would have been if they hadn’t. If that’s not the case, we will make significant changes (and if we can’t fix it, we would discontinue offering the service).

*Encourage users to control their feed. You should be able to tell Sora what you want—do you want to see videos that will make you more relaxed, or more energized? Or only videos that fit a specific interest? Or only for a certain about of time? Eventually as our technology progresses, you will be should to the tell Sora what you want in detail in natural language. (However, parental controls for teens include the ability to opt out of a personalized feed, and other things like turning off DMs.)

*Prioritize creation. We want to make it easy and rewarding for everyone to participate in the creation process; we believe people are natural-born creators, and creating is important to our satisfaction.

*Help users achieve their long-term goals. We want to understand a user’s true goals, and help them achieve them. If you want to be more connected to your friends, we will try to help you with that. If you want to get fit, we can show you fitness content that will motivate you. If you want to start a business, we want to help teach you the skills you need. And if you truly just want to doom scroll and be angry, then ok, we’ll help you with that (although we want users to spend time using the app if they think it’s time well spent, we don’t want to be paternalistic about what that means to them).

丰沛智能 || Abundant Intelligence

2025-09-24T01:02:23+00:00

人工智能服务的使用增长令人震惊，我们预计未来这一趋势将更加惊人。随着AI变得更智能，获取AI技术将成为经济的核心驱动力，甚至可能最终被视为一项基本人权。几乎所有人都希望AI能为自己提供更多服务。

为了满足全球需求——既需要运行这些模型的推理算力，也需要持续改进模型的训练算力——我们正在为大幅扩展AI基础设施的宏伟计划奠定基础。如果AI按我们预期的轨迹发展，将可能实现惊人突破：或许10吉瓦的算力能让AI找到治愈癌症的方法，或是为地球上的每个学生提供个性化辅导。若受限于算力，我们将被迫做出优先级选择；但没人愿意面临这种取舍，所以我们必须行动起来。

我们的愿景很简单：打造一个每周能生产1吉瓦AI新基础设施的"工厂"。实现这一目标极具挑战性，需要多年时间，并在芯片、能源、建筑、机器人等全产业链实现创新。但我们已全力投入，并坚信其可行性。这将是史上最酷且最重要的基础设施项目。

我们尤其期待在美国建设大量设施。当前其他国家在芯片工厂和新能源生产等领域的建设速度远超美国，我们希望能扭转这一局面。未来几个月，我们将公布具体计划和合作伙伴；今年晚些时候将探讨融资方案——鉴于增加算力直接关乎收入增长，我们已构思了一些创新思路。

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AI服务的使用增长令人惊叹；我们预计未来这一趋势将更加惊人。

随着AI日益智能化，获取AI技术将成为经济发展的核心驱动力，或许最终会被视为一项基本人权。几乎每个人都希望有更多AI为自己服务。

为了满足全球需求——既要运行这些模型的推理计算，又要通过训练计算持续优化它们——我们正在奠定基础，以大幅扩展建设AI基础设施的宏伟计划。

如果AI沿着我们预期的轨迹发展，将可能实现非凡成就。或许10吉瓦的计算能力能让AI找到治愈癌症的方法，或是为地球上每个学生提供个性化辅导。若受限于计算资源，我们将被迫做出优先级选择；没人愿意面临这种抉择，所以让我们行动起来共同建设。

我们的愿景很简单：打造一个每周能生产1吉瓦级AI基础设施的超级工厂。实现这一目标将极具挑战性，需要多年时间达成里程碑，并在从芯片到能源、从建筑到机器人技术的全产业链实现创新。但我们已为此付出巨大努力，并坚信其可行性。在我们看来，这将是史上最酷且最重要的基础设施项目。我们尤其期待在美国本土大量建设这类设施；当前其他国家在芯片工厂和新能源生产等领域的建设速度远超我们，我们希望能助力扭转这一局面。

接下来几个月，我们将陆续公布部分计划及合作方信息。今年晚些时候，我们会探讨融资方案；鉴于提升计算能力直接关乎收入增长，我们有些令人耳目一新的创新思路。

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Growth in the use of AI services has been astonishing; we expect it to be even more astonishing going forward.

As AI gets smarter, access to AI will be a fundamental driver of the economy, and maybe eventually something we consider a fundamental human right. Almost everyone will want more AI working on their behalf.

To be able to deliver what the world needs—for inference compute to run these models, and for training compute to keep making them better and better—we are putting the groundwork in place to be able to significantly expand our ambitions for building out AI infrastructure.

If AI stays on the trajectory that we think it will, then amazing things will be possible. Maybe with 10 gigawatts of compute, AI can figure out how to cure cancer. Or with 10 gigawatts of compute, AI can figure out how to provide customized tutoring to every student on earth. If we are limited by compute, we’ll have to choose which one to prioritize; no one wants to make that choice, so let’s go build.

Our vision is simple: we want to create a factory that can produce a gigawatt of new AI infrastructure every week. The execution of this will be extremely difficult; it will take us years to get to this milestone and it will require innovation at every level of the stack, from chips to power to building to robotics. But we have been hard at work on this and believe it is possible. In our opinion, it will be the coolest and most important infrastructure project ever. We are particularly excited to build a lot of this in the US; right now, other countries are building things like chips fabs and new energy production much faster than we are, and we want to help turn that tide.

Over the next couple of months, we’ll be talking about some of our plans and the partners we are working with to make this a reality. Later this year, we’ll talk about how we are financing it; given how increasing compute is the literal key to increasing revenue, we have some interesting new ideas.

雅库布和西蒙 || Jakub and Szymon

2025-09-23T04:40:10+00:00

近年来，AI技术突飞猛进——ChatGPT展现的惊人能力已让我们习以为常。这本就是人类进步的常态。但在那个闪烁的光标背后，隐藏着人类智慧最伟大的篇章：无数人付出了难以想象的努力，实现了多数专家认为短期内不可能完成的任务，并创建了规模化交付产品的公司，让全球受益。

虽然大多数ChatGPT用户不会深究背后的付出（这完全合理），但请容我占用一分钟，致敬两位OpenAI不可或缺的灵魂人物：**Jakub Pachocki**和**Szymon Sidor**。他们屡次以「科研+工程」的复合能力攻克难题，却未获得足够的公众赞誉。例如：
- 当学界普遍认为强化学习（RL）无法扩展时，他们坚持推进并发现其边界，最终成就了Dota项目；
- 搭建了支撑多项科学发现的基础设施；
- 领导GPT-4预训练；
- 与Ilya、Lukasz共同开创了推理突破的初始构想；
- 在新范式探索中取得重大进展。

Jakub现任OpenAI首席科学家，他曾用"**indefatigable（不知疲倦）**"形容Szymon——这个词的精准运用令我叹服。至今OpenAI尚未抛出他们无法解决的难题。这种如同昔日实验室传奇般的黄金搭档，能在多年间持续互补共进，实属罕见而珍贵。

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近年来，人工智能取得了显著进步；ChatGPT能完成许多我们视为理所当然的惊人任务。这本就是技术发展的应有之义，也是人类进步的故事。但在那闪烁的光标背后，被优雅封装起来的，是我所见过最伟大的人类智慧结晶。无数人付出了难以想象的努力，在多数专家认为不可能的时间框架内实现了技术突破，并创建公司大规模交付产品，让世人受益。大多数ChatGPT用户永远不会想到幕后付出艰辛的人们，这完全正常，但请允许我占用您一分钟时间……

有两位不可或缺的人物让OpenAI成为今天的OpenAI：雅库布·帕霍茨基和希蒙·西多尔。他们屡次将研究与工程相结合，攻克了看似无解的难题。虽然公众给予的赞誉远不足够，但正是他们决定将强化学习作为基线进行扩展测试——当时普遍认为这是不可扩展的——最终成就了我们在Dota项目上的突破；他们搭建的基础设施支撑了大量科学发现，主导了GPT-4的预训练，与伊利亚和卢卡什共同催生了带来推理突破的初始构想，并在探索新范式方面取得重大进展。

雅库布是我们的首席科学家。他曾用"不知疲倦"形容希蒙，这是我听过对这个词最完美的诠释。OpenAI尚未提出过他们无法解决的难题；我听说过历史上研究实验室里那种珠联璧合的合作伙伴关系，但能亲眼见证这种默契多年来的发展，实属难得。

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AI has gotten remarkably better in recent years; ChatGPT can do amazing things that we take for granted. This is as it should be, and is the story of human progress. But behind the blinking circle, nicely abstracted away, is the greatest story of human ingenuity I have ever seen. A lot of people have worked unbelievably hard to discover how to build something that most experts thought was impossible on this timeframe, and to build a company to deliver products at massive scale to let people benefit from it. Most people who use ChatGPT will never think about the people that put so much work into it, which is totally ok, but just to take a minute of your time…

There are two people I'd like to mention that OpenAI would not be OpenAI without: Jakub Pachocki and Szymon Sidor. Time and again, they combine research and engineering to solve impossible problems. They have not gotten enough public credit, but they decided to scale up RL as a baseline to see where it broke when the conventional wisdom was that it didn't scale which led to our Dota result, built much of the infrastructure that enabled a lot of our scientific discoveries, led GPT-4 pretraining, drove together with Ilya and Lukasz the initial ideas that led to the reasoning breakthrough, and have made significant progress exploring new paradigms.

Jakub is our chief scientist. He once described Szymon as “indefatigable”, which is as perfect of a use of that word as I have ever heard. OpenAI has not yet thrown a problem at them they have not been able to solve; I have heard about partnerships like there is research labs of the past where two people are able to complement each other so well, but it is very special to get to watch it unfold over the years.

温柔奇点 || The Gentle Singularity

2025-09-23T06:37:33+00:00

# 超越奇点：AI时代的加速未来

## 已跨越临界点
人类已跨过技术奇点的门槛，数字超级智能的诞生近在咫尺。尽管当前世界尚未出现机器人满街走的科幻场景，但GPT-4等系统的突破性进展证明：**我们已创造出在多方面超越人类智能的AI工具**。这些系统能显著放大使用者的生产力，而最艰难的科学突破阶段已经过去。

## 指数级演进路线图
- **2025年**：可完成真实认知工作的AI代理将重塑编程领域  
- **2026年**：具备自主发现新知识能力的系统问世  
- **2027年**：能执行实体任务的机器人可能出现  
到2030年，单个人的生产力将出现**数量级跃升**，艺术与软件的创作门槛将大幅降低，但专家仍将保持优势——前提是善用新工具。

## 双面革命
**表层生活**：家庭、娱乐、自然体验等人类本质需求不会剧变  
**深层变革**：智力与能源（实现想法的两大基础要素）将变得极度丰富，突破长期制约人类发展的根本瓶颈。AI已使科学家效率提升2-3倍，更关键的是：**AI正加速AI研究本身**，形成递归式进步循环。

## 经济奇点临近
- 数据中心自动化将使智能成本趋近于电力成本（当前ChatGPT单次查询耗能≈0.34瓦时）  
- 机器人制造机器人的产业链一旦形成，进步速度将呈指数级跃升  
- 技术变革可能催生全新的社会契约与政策范式

## 挑战与机遇并存
**关键任务清单**：  
1. **解决对齐问题**：确保AI系统长期符合人类集体利益（避免重蹈社交媒体算法短视优化的覆辙）  
2. **分布式普及**：使超级智能廉价、广泛可用且权力不过度集中  

## 未来图景
- 2035年前可能相继突破高能物理、太空殖民、脑机接口等领域  
- 人类将保持对AI的独特优势：**与生俱来的人际关怀**（这是AI难以复制的特质）  
- 工作形态将持续演化（如同工业革命后的职业变迁），未来职业在当下看来可能如同"虚假工作"  

## 行动纲领
OpenAI定位为**超级智能研究公司**，致力于构建"世界大脑"。行业需要：  
- 在保障安全的前提下最大化技术自由度  
- 尽快启动关于AI伦理边界的社会共识讨论  
- 释放"创意者"的潜力（技术实现门槛降低后，优质想法将成为核心稀缺资源）  

> "如果2020年预测当前成就显得疯狂，那么今天对2030年的展望或许同样如此——但历史证明，我们总能适应技术的加速。"  

**最终目标**：实现"廉价到可忽略的智能"，让人类在超级智能时代平稳驶向更美好的未来。

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我们已跨越事件视界，起飞正式启动。人类距离构建数字超级智能仅一步之遥，而迄今为止，其发展轨迹远比预想中更为平顺。

机器人尚未漫步街头，多数人也未整日与AI对话。疾病仍在夺走生命，太空旅行依旧艰难，宇宙中仍有无数未解之谜。

然而，我们已创造出在多方面超越人类智慧的体系，能显著放大使用者的产出效能。最艰难的科学突破——如GPT-4和o3系统的诞生——已成过往，这些硬仗赢得的认知将引领我们走得更远。

AI将以多种方式造福世界，但最重大的贡献在于加速科学进步与提升生产力带来的生活质量跃升。未来可能远比现在美好，因为科学突破始终是文明进步的核心引擎——想到我们即将拥有的可能性，实在令人振奋。

从某种宏大意义上说，ChatGPT已比历史上任何人类都更强大。数亿人每天依赖它处理日益重要的事务：微小新功能可能产生巨大积极影响，细微偏差经数亿倍放大也会造成严重负面效应。

2025年，能完成真实认知工作的智能体已然问世，编程领域永远改变。2026年或将出现能发现新颖洞见的系统，2027年可能迎来能在现实世界执行任务的机器人。

更多人将能创作软件与艺术。但世界对二者的需求永无止境，只要善用新工具，专家仍将远胜新手。到2030年，个人生产力相较2020年的跃升将形成震撼变革，无数人将从中获益。

在最本质的层面，2030年代或许不会天翻地覆：人们仍会热爱家庭、挥洒创意、享受游戏、湖畔畅游。

但在同样关键的维度，2030年代或将前所未有地颠覆认知。我们尚不知智能水平能超越人类多远，但答案即将揭晓。

2030年代，智力与能源——即创意与实现创意的能力——将变得极度丰沛。这两者长期制约着人类发展，而充足的智能与能源（辅以良治）理论上能带来一切。

我们已与惊人数字智能共存，经历短暂震撼后大多习以为常。转瞬间，AI从生成优美段落到创作完整小说，从诊断疾病到研发疗法，从编写小程序到创立新公司——奇点降临的轨迹总是如此：奇迹先成常态，再变基础配置。

科学家们自述生产力已提升2-3倍。先进AI最深远的意义或许在于它能加速AI研究本身：新计算基质、更优算法，乃至未知突破。若能将十年研究压缩至一年甚至一月，进步速率将彻底改变。

既有工具将助我们发现新科学洞见，构建更优AI系统。虽非完全自主的代码演进，但这已是递归自我改进的雏形。

其他增强循环也在运转：经济价值创造正推动AI基础设施的复合增长。能自我复制的机器人（某种意义上也包括数据中心）已不遥远。

若首批百万仿生机器人需传统方式制造，之后它们便可自主运营整个供应链——采矿冶炼、驾驶运输、工厂运营等——进而制造更多机器人，建设芯片厂与数据中心，进步速率将截然不同。

随着数据中心生产自动化，智能成本终将趋近电力成本。（人们常好奇ChatGPT查询耗能：单次约0.34瓦时，相当于烤箱运作一秒或多或高效灯泡两分钟，耗水约0.000085加仑，近乎十五分之一茶匙。）

技术进步将持续加速，人类适应力始终在线。尽管某些职业将整体消失，但世界财富的极速增长将使我们能尝试前所未有的新政。社会契约不会瞬间重构，但数十年后回望，渐变累积终成巨变。

历史表明，我们总能找到新需求，快速吸纳新工具（工业革命后的职业变迁即为例证）。期待值与能力同步跃升，我们将为彼此创造更美好的事物。人类相较AI存在根本优势：我们天生在意他人所思所为，而非机器。

千年前的农夫会视现代工作为"虚假职业"，认为我们因丰衣足食而游戏人生。但愿未来千年后的职业同样被视作"虚假"，但对从业者而言，它们必将充满意义与满足。

奇迹诞生的速度将超乎想象。今日甚至难以构想2035年的突破：或许今年解决高能物理，明年开启太空殖民；或今年材料科学突破，明年实现高带宽脑机接口。虽多数人生活方式依旧，但必有人选择"接入"新维度。

前瞻未来令人目眩，但亲历过程或将从容。从相对论视角看，奇点是渐进过程，融合缓慢发生。我们正攀登技术进步的指数曲线——前瞻时似垂直陡壁，回望时如平坦大道。（试想2020年预言2025年接近AGI有多疯狂，对比过去五年的实际发展。）

伴随巨大机遇的是严峻挑战。我们需从技术和社会层面解决安全问题，但鉴于经济影响，广泛普及超级智能访问权同样关键。最佳路径或许是：

解决对齐问题，确保AI系统长期稳定地学习并践行人类集体真实意愿（社交媒体推荐即未对齐案例：算法精于捕捉短期偏好让你持续刷屏，却利用了违背你长期利益的认知弱点）。
致力于使超级智能廉价、普及且不过度集中于个体、企业或国家。社会具备韧性、创造力与快速适应力。若能凝聚集体智慧，纵有失误与挫折，我们终将快速学习调整，最大化技术收益，最小化负面影响。在社会共识的宽泛边界内赋予用户充分自由至关重要。世界越早开始探讨这些边界与集体对齐的定义越好。

我们（整个行业，不止OpenAI）正在为世界构建大脑。它将极度个性化且易用，唯一限制将是好创意。长期以来，科技创业者常嘲笑"点子王"——那些只有创意却无执行团队的人。现在看来，他们的高光时刻即将到来。

OpenAI如今有多重身份，但首先是超级智能研究公司。前路虽仍有挑战，但多数路径已然明朗，黑暗区域正快速消退。能从事这份事业，我们深感荣幸。

近乎免费的智能触手可及。此言或许惊人，但若在2020年预言今日成就，恐怕比如今对2030年的预测更显荒诞。

愿我们在超级智能时代平稳、指数级且波澜不惊地攀登高峰。

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We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be.

Robots are not yet walking the streets, nor are most of us talking to AI all day. People still die of disease, we still can’t easily go to space, and there is a lot about the universe we don’t understand.

And yet, we have recently built systems that are smarter than people in many ways, and are able to significantly amplify the output of people using them. The least-likely part of the work is behind us; the scientific insights that got us to systems like GPT-4 and o3 were hard-won, but will take us very far.

AI will contribute to the world in many ways, but the gains to quality of life from AI driving faster scientific progress and increased productivity will be enormous; the future can be vastly better than the present. Scientific progress is the biggest driver of overall progress; it’s hugely exciting to think about how much more we could have.

In some big sense, ChatGPT is already more powerful than any human who has ever lived. Hundreds of millions of people rely on it every day and for increasingly important tasks; a small new capability can create a hugely positive impact; a small misalignment multiplied by hundreds of millions of people can cause a great deal of negative impact.

2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same. 2026 will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world.

A lot more people will be able to create software, and art. But the world wants a lot more of both, and experts will probably still be much better than novices, as long as they embrace the new tools. Generally speaking, the ability for one person to get much more done in 2030 than they could in 2020 will be a striking change, and one many people will figure out how to benefit from.

In the most important ways, the 2030s may not be wildly different. People will still love their families, express their creativity, play games, and swim in lakes.

But in still-very-important-ways, the 2030s are likely going to be wildly different from any time that has come before. We do not know how far beyond human-level intelligence we can go, but we are about to find out.

In the 2030s, intelligence and energy—ideas, and the ability to make ideas happen—are going to become wildly abundant. These two have been the fundamental limiters on human progress for a long time; with abundant intelligence and energy (and good governance), we can theoretically have anything else.

Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes.

We already hear from scientists that they are two or three times more productive than they were before AI. Advanced AI is interesting for many reasons, but perhaps nothing is quite as significant as the fact that we can use it to do faster AI research. We may be able to discover new computing substrates, better algorithms, and who knows what else. If we can do a decade’s worth of research in a year, or a month, then the rate of progress will obviously be quite different.

From here on, the tools we have already built will help us find further scientific insights and aid us in creating better AI systems. Of course this isn’t the same thing as an AI system completely autonomously updating its own code, but nevertheless this is a larval version of recursive self-improvement.

There are other self-reinforcing loops at play. The economic value creation has started a flywheel of compounding infrastructure buildout to run these increasingly-powerful AI systems. And robots that can build other robots (and in some sense, datacenters that can build other datacenters) aren’t that far off.

If we have to make the first million humanoid robots the old-fashioned way, but then they can operate the entire supply chain—digging and refining minerals, driving trucks, running factories, etc.—to build more robots, which can build more chip fabrication facilities, data centers, etc, then the rate of progress will obviously be quite different.

As datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity. (People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.)

The rate of technological progress will keep accelerating, and it will continue to be the case that people are capable of adapting to almost anything. There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before. We probably won’t adopt a new social contract all at once, but when we look back in a few decades, the gradual changes will have amounted to something big.

If history is any guide, we will figure out new things to do and new things to want, and assimilate new tools quickly (job change after the industrial revolution is a good recent example). Expectations will go up, but capabilities will go up equally quickly, and we’ll all get better stuff. We will build ever-more-wonderful things for each other. People have a long-term important and curious advantage over AI: we are hard-wired to care about other people and what they think and do, and we don’t care very much about machines.

A subsistence farmer from a thousand years ago would look at what many of us do and say we have fake jobs, and think that we are just playing games to entertain ourselves since we have plenty of food and unimaginable luxuries. I hope we will look at the jobs a thousand years in the future and think they are very fake jobs, and I have no doubt they will feel incredibly important and satisfying to the people doing them.

The rate of new wonders being achieved will be immense. It’s hard to even imagine today what we will have discovered by 2035; maybe we will go from solving high-energy physics one year to beginning space colonization the next year; or from a major materials science breakthrough one year to true high-bandwidth brain-computer interfaces the next year. Many people will choose to live their lives in much the same way, but at least some people will probably decide to “plug in”.

Looking forward, this sounds hard to wrap our heads around. But probably living through it will feel impressive but manageable. From a relativistic perspective, the singularity happens bit by bit, and the merge happens slowly. We are climbing the long arc of exponential technological progress; it always looks vertical looking forward and flat going backwards, but it’s one smooth curve. (Think back to 2020, and what it would have sounded like to have something close to AGI by 2025, versus what the last 5 years have actually been like.)

There are serious challenges to confront along with the huge upsides. We do need to solve the safety issues, technically and societally, but then it’s critically important to widely distribute access to superintelligence given the economic implications. The best path forward might be something like:

Solve the alignment problem, meaning that we can robustly guarantee that we get AI systems to learn and act towards what we collectively really want over the long-term (social media feeds are an example of misaligned AI; the algorithms that power those are incredible at getting you to keep scrolling and clearly understand your short-term preferences, but they do so by exploiting something in your brain that overrides your long-term preference).
Then focus on making superintelligence cheap, widely available, and not too concentrated with any person, company, or country. Society is resilient, creative, and adapts quickly. If we can harness the collective will and wisdom of people, then although we’ll make plenty of mistakes and some things will go really wrong, we will learn and adapt quickly and be able to use this technology to get maximum upside and minimal downside. Giving users a lot of freedom, within broad bounds society has to decide on, seems very important. The sooner the world can start a conversation about what these broad bounds are and how we define collective alignment, the better.

We (the whole industry, not just OpenAI) are building a brain for the world. It will be extremely personalized and easy for everyone to use; we will be limited by good ideas. For a long time, technical people in the startup industry have made fun of “the idea guys”; people who had an idea and were looking for a team to build it. It now looks to me like they are about to have their day in the sun.

OpenAI is a lot of things now, but before anything else, we are a superintelligence research company. We have a lot of work in front of us, but most of the path in front of us is now lit, and the dark areas are receding fast. We feel extraordinarily grateful to get to do what we do.

Intelligence too cheap to meter is well within grasp. This may sound crazy to say, but if we told you back in 2020 we were going to be where we are today, it probably sounded more crazy than our current predictions about 2030.

May we scale smoothly, exponentially and uneventfully through superintelligence.

三思而行 || Three Observations

2025-09-22T14:52:03+00:00

我们的使命是确保人工通用智能（AGI）造福全人类。  
初现AGI雏形的系统正在形成，因此理解当下至关重要。AGI虽定义模糊，但通常指能在多领域以人类水平解决复杂问题的系统。  

人类天生渴望探索与创造，代代传承的发明（电力、晶体管、计算机、互联网，直至未来的AGI）推动世界进步。尽管进程曲折，创新持续带来难以想象的繁荣与生活改善。AGI既是人类进步阶梯中的新工具，又可能标志着一个"这次不同"的时代——经济增长或将颠覆想象：治愈所有疾病、更多家庭时光、释放创造力。十年后，普通人成就或超越当今最具影响力者。  

### 关于AI经济的三点观察：  
1. **智能与资源对数相关**：训练算力、数据与推理资源投入越多，模型智能提升越可预测。  
2. **成本骤降，应用激增**：AI使用成本每12个月下降10倍（如GPT-4到GPT-4o的token成本降150倍），远超摩尔定律。  
3. **智能的 socioeconomic 价值呈超指数增长**：指数级投资增长短期内无停止迹象。  

### 未来图景：  
- **AI智能体**将如虚拟同事渗透各领域。例如，软件工程AI可完成资深工程师数日任务（需人类监督），百万量级应用将重塑知识工作。  
- AI或如晶体管般渗透经济各角落，效益广泛分配但隐性存在。  

### 社会影响：  
- 短期生活变化有限，但长期变革不可忽视。  
- **适应力与决策力**成为核心价值，AGI将放大个人影响力。  
- 影响不均衡：科研加速，商品价格暴跌（智力与能源成本下降），奢侈品与土地价格或飙升。  

### 政策与技术协同：  
- 早期产品发布旨在促进社会与技术共同进化。  
- 需平衡安全与个体赋权，警惕威权政府滥用AI。  
- 广泛分配AGI收益是关键，可能需创新方案（如全球"算力预算"）。  

2035年，每人或可调动2025年全人类的智力资源。释放未被开发的创造力，将惠及全球。  

*注：本文使用AGI术语仅为清晰表达，不改变与微软合作的定义与进程。我们期待与微软长期合作。此脚注看似多余，实为预判某些媒体的夸张解读。*

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我们的使命是确保人工通用智能（AGI）惠及全人类。

指向AGI*的系统已初现端倪，因此理解当下这一关键时刻至关重要。AGI虽定义模糊，但通常指能在多领域以人类水平解决日益复杂问题的系统。

人类天生具有理解与创造的工具制造本能，这种驱动力推动世界不断进步。每一代人都站在前人的发现之上，创造出更强大的工具——电力、晶体管、计算机、互联网，以及即将到来的AGI。

历经跌宕起伏，人类创新的稳步前行已将难以想象的繁荣与生活各领域的改善带到我们面前。

从某种角度看，AGI只是人类共同搭建的进步阶梯中又一新工具；但从另一角度看，我们很难不感叹"这次不同以往"——眼前的经济增长令人震撼，我们已能构想一个治愈所有疾病、拥有更多家庭时光、充分释放创造潜能的世界。

十年后，或许地球上的每个人都能超越当今最具影响力人物的成就。

AI发展持续迅猛，以下是关于AI经济学的三点观察：

1. AI模型的智能水平大致与其训练和运行资源消耗的对数成正比。 这些资源主要包括训练算力、数据和推理算力。事实证明，投入任意资金都能获得持续可预测的收益，这种规模定律在多个数量级上都精确成立。

2. 固定AI水平的使用成本每12个月下降约10倍，低价催生更广泛应用。 从2023年初GPT-4到2024年中GPT-4o，单token价格下降约150倍。相比摩尔定律18个月翻倍的变革力，这堪称惊人。

3. 线性增长的智能所产生的社会经济价值本质上是超指数级的。 这意味着我们没有理由认为指数级增长的投资会在近期停止。

若这三点观察持续成立，社会影响将极为深远。

我们正开始部署AI智能体，它们终将成为虚拟同事。

以软件工程智能体为例——这类我们预期特别重要的智能体。想象它能完成顶尖公司数年经验工程师数日内的多数工作，虽缺乏突破性创意、需要大量人工指导，且能力存在明显波动。

不妨将其视为真实但资历尚浅的虚拟同事。再想象拥有1000个，或100万个这样的同事。进而推及所有知识工作领域。

经济层面，AI或许会像晶体管——一项渗透经济各角落的重大科学发现。我们很少思考晶体管或其公司，但广泛享受着它带来的奇迹。

世界不会骤然改变。短期内生活依旧，2025年人们的时间分配与2024年大同小异。我们仍会恋爱成家、网络争论、徒步自然。

但未来将以不可忽视的方式降临，社会经济将经历深刻变革。人类将发现新的存在价值、互助方式和竞争形态，它们可能与当今职业大相径庭。

自主性、意志力和决断力将弥足珍贵。在瞬息万变的世界中正确决策将价值连城，韧性与适应力成为关键技能。AGI将成为人类意志的最大杠杆，让个体产生空前而非减弱的影响力。

AGI的影响将不均衡。虽然某些行业变化甚微，但科学进步可能远超今日——这一影响或许超越其他所有领域。

多数商品价格终将大幅下降（当前受限于智能与能源成本），而奢侈品及土地等有限资源的价格可能飙升。

技术路径已然清晰，但AGI的社会整合政策与共识至关重要。我们频繁推出产品的原因之一，正是为了让社会与技术协同进化。

AI将渗透经济社会各领域，万物都将智能化。相比历史做法，我们预期需要赋予人们更多技术控制权，包括更多开源，在安全与个体赋权间寻求平衡。

尽管我们绝不轻率，但某些AGI安全相关的重大决策难免引发争议。总体而言，随着AGI临近，我们认为个体赋权趋势至关重要——另一可能路径是威权政府通过AI实施大规模监控、剥夺民众自主权。

确保AGI收益广泛分配是关键。历史表明技术进步平均改善健康、经济等指标，但平等性提升非技术必然，需要创新思路。

尤其资本与劳动力的力量平衡极易失调，可能需要早期干预。我们愿尝试非常规方案，如分配"算力预算"让全球民众共享AI，但也看到持续降低智能成本的显著效果。

到2035年，每个人都应能调动相当于2025年全人类的智力资源，获得无限天才的指引。当今无数天赋因资源限制未能绽放，若改变这点，世界将因创造力爆发而共同受益。

特别感谢Josh Achiam、Boaz Barak和Aleksander Madry审阅本文草稿。

*此处使用AGI术语仅为清晰表述，无意改变或解释我们与微软合作关系的定义及流程。我们期待与微软建立长期伙伴关系。本注看似多余，但鉴于某些媒体会为点击率制造噱头，我们先行预防...

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Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity.

Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields.

People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all of us. Each new generation builds upon the discoveries of the generations before to create even more capable tools—electricity, the transistor, the computer, the internet, and soon AGI.

Over time, in fits and starts, the steady march of human innovation has brought previously unimaginable levels of prosperity and improvements to almost every aspect of people’s lives.

In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together. In another sense, it is the beginning of something for which it’s hard not to say “this time it’s different”; the economic growth in front of us looks astonishing, and we can now imagine a world where we cure all diseases, have much more time to enjoy with our families, and can fully realize our creative potential.

In a decade, perhaps everyone on earth will be capable of accomplishing more than the most impactful person can today.

We continue to see rapid progress with AI development. Here are three observations about the economics of AI:

1. The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude.

2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time period. Moore’s law changed the world at 2x every 18 months; this is unbelievably stronger.

3. The socioeconomic value of linearly increasing intelligence is super-exponential in nature. A consequence of this is that we see no reason for exponentially increasing investment to stop in the near future.

If these three observations continue to hold true, the impacts on society will be significant.

We are now starting to roll out AI agents, which will eventually feel like virtual co-workers.

Let’s imagine the case of a software engineering agent, which is an agent that we expect to be particularly important. Imagine that this agent will eventually be capable of doing most things a software engineer at a top company with a few years of experience could do, for tasks up to a couple of days long. It will not have the biggest new ideas, it will require lots of human supervision and direction, and it will be great at some things but surprisingly bad at others.

Still, imagine it as a real-but-relatively-junior virtual coworker. Now imagine 1,000 of them. Or 1 million of them. Now imagine such agents in every field of knowledge work.

In some ways, AI may turn out to be like the transistor economically—a big scientific discovery that scales well and that seeps into almost every corner of the economy. We don’t think much about transistors, or transistor companies, and the gains are very widely distributed. But we do expect our computers, TVs, cars, toys, and more to perform miracles.

The world will not change all at once; it never does. Life will go on mostly the same in the short run, and people in 2025 will mostly spend their time in the same way they did in 2024. We will still fall in love, create families, get in fights online, hike in nature, etc.

But the future will be coming at us in a way that is impossible to ignore, and the long-term changes to our society and economy will be huge. We will find new things to do, new ways to be useful to each other, and new ways to compete, but they may not look very much like the jobs of today.

Agency, willfulness, and determination will likely be extremely valuable. Correctly deciding what to do and figuring out how to navigate an ever-changing world will have huge value; resilience and adaptability will be helpful skills to cultivate. AGI will be the biggest lever ever on human willfulness, and enable individual people to have more impact than ever before, not less.

We expect the impact of AGI to be uneven. Although some industries will change very little, scientific progress will likely be much faster than it is today; this impact of AGI may surpass everything else.

The price of many goods will eventually fall dramatically (right now, the cost of intelligence and the cost of energy constrain a lot of things), and the price of luxury goods and a few inherently limited resources like land may rise even more dramatically.

Technically speaking, the road in front of us looks fairly clear. But public policy and collective opinion on how we should integrate AGI into society matter a lot; one of our reasons for launching products early and often is to give society and the technology time to co-evolve.

AI will seep into all areas of the economy and society; we will expect everything to be smart. Many of us expect to need to give people more control over the technology than we have historically, including open-sourcing more, and accept that there is a balance between safety and individual empowerment that will require trade-offs.

While we never want to be reckless and there will likely be some major decisions and limitations related to AGI safety that will be unpopular, directionally, as we get closer to achieving AGI, we believe that trending more towards individual empowerment is important; the other likely path we can see is AI being used by authoritarian governments to control their population through mass surveillance and loss of autonomy.

Ensuring that the benefits of AGI are broadly distributed is critical. The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas.

In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention. We are open to strange-sounding ideas like giving some “compute budget” to enable everyone on Earth to use a lot of AI, but we can also see a lot of ways where just relentlessly driving the cost of intelligence as low as possible has the desired effect.

Anyone in 2035 should be able to marshall the intellectual capacity equivalent to everyone in 2025; everyone should have access to unlimited genius to direct however they can imagine. There is a great deal of talent right now without the resources to fully express itself, and if we change that, the resulting creative output of the world will lead to tremendous benefits for us all.

Thanks especially to Josh Achiam, Boaz Barak and Aleksander Madry for reviewing drafts of this.

*By using the term AGI here, we aim to communicate clearly, and we do not intend to alter or interpret the definitions and processes that define our relationship with Microsoft. We fully expect to be partnered with Microsoft for the long term. This footnote seems silly, but on the other hand we know some journalists will try to get clicks by writing something silly so here we are pre-empting the silliness…

反思 || Reflections

2025-09-22T23:59:01+00:00

# ChatGPT两周年回顾：通往AGI的荆棘与荣光

距离ChatGPT两岁生日刚过月余，我们已迈入具备复杂推理能力的新模型时代。值此辞旧迎新之际，我想分享些个人思考：

## 初心与蜕变
九年前创立OpenAI时，我们坚信AGI将是人类史上最具影响力的技术。尽管当时鲜有人看好，2022年那个临时命名为"Chat With GPT-3.5"的安静实验室，却在11月30日意外引爆了AI革命。ChatGPT的增长曲线颠覆了公司、行业乃至整个世界的认知。

## 成长的阵痛
过去两年犹如在未知海域全速航行：
- 从零构建新型科技公司的过程充满试错（常是进二退一，有时进一退二）
- 去年那场突如其来的公开解雇事件，暴露出善意治理的失败
- 团队规模每数月数量级增长，自然伴随人员流动与竞争分化

这段经历汇集了我人生中最充实、最有趣也最煎熬的时光。特别感谢危机中伸出援手的Ron Conway、Brian Chesky等伙伴，他们的战略智慧与人情温度让OpenAI免于分崩离析。

## 当下与未来
我们已达成的重要里程碑：
- 周活跃用户从1亿增至3亿
- 持续产出人们真心热爱的实用技术
- 确立了安全部署AI的渐进式路径

展望2025年，首批"AI员工"或将实质性改变企业生产力。而真正的星辰大海在于超级智能——它能指数级加速科学发现，创造前所未有的繁荣。尽管这听起来仍像科幻，但我们已习惯从"疯狂设想"走向"普遍认知"。

## 不变的使命
OpenAI注定不是普通公司：
- 战术会变（如意外转型产品公司），但造福人类的愿景永恒
- 坚持通过现实反馈迭代安全研究
- 超级智能时代需要非凡的谨慎与担当

能参与这项事业何其幸运。前路仍有无数未知，但比起九年前，我们已看清更多可能。

（注：原文约3000词，中文摘要压缩至约800字，保留核心叙事线与关键细节，采用模块化结构增强可读性，重点数据及人物事例予以保留）

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ChatGPT的第二个生日才过去一个多月，如今我们已迈入能进行复杂推理的新一代模型范式。岁末年初总让人思绪万千，我想分享一些个人感悟——关于这段旅程的进展，以及沿途收获的启示。

随着AGI（通用人工智能）的临近，审视公司发展历程显得尤为重要。我们仍有太多未知，一切才刚刚开始。但与初创时期相比，认知已不可同日而语。

九年前创立OpenAI时，我们坚信AGI将成为人类史上最具颠覆性的技术。我们渴望探索其构建之道并普惠众生，这种改写历史的可能性令我们心潮澎湃。当时的抱负与信念同样宏大——这项事业可能以空前方式造福社会。

那时鲜有人关注，即便有也多是认为我们注定失败。

2022年的OpenAI还是个安静的研究实验室，正开发暂名"与GPT-3.5对话"的项目（我们擅长研究远胜命名）。通过API游乐场，我们观察到开发者们与模型对话的浓厚兴趣。于是决定打造演示版，既展示未来图景，也助力模型优化与安全提升。

最终我们庆幸地将其命名为ChatGPT，并于2022年11月30日发布。

我们始终明白某个临界点将引爆AI革命，但未曾预料到竟是此刻。ChatGPT的爆发式增长曲线颠覆了公司、行业乃至整个世界的认知尺度。AI长期期盼的巨大价值正逐步显现，更多突破已清晰可期。

这绝非坦途。抉择从未明晰，道路始终崎岖。

过去两年，我们几乎从零构建了围绕新技术的完整公司体系。在全新领域，唯有实践方能育人，无人能给出确凿指南。

高速初创注定混乱——常是进二退一，有时进一步退两步。原创性工作中没有现成手册，未知水域的疾驰令人振奋却也压力倍增。冲突与误解如影随形。

这些年是我生命中最充实、有趣、精彩又疲惫、紧张——尤其是最近两年颇为煎熬的时光。但充盈心间的仍是感恩：终有一天，当我在牧场闲看草木生长时，会怀念这段实现儿时梦想的奇幻旅程。每个危机四伏的周五下午，我总如此提醒自己。

去年某个周五，最糟的意外是通过视频会议突遭解雇，紧接着董事会公告全网发布。拉斯维加斯酒店房间里，那种超现实的荒诞感至今难以言表。

毫无预警的公开解雇引发数日混乱。"战争迷雾"最为诡异——无人能说清事件原委。

这本质上是治理机制的失败，包括我在内的善意者都难辞其咎。如今回望，我本可做得更好，也自认比一年前更成熟睿智。

这段经历让我深刻认识到：多元观点与丰富经验的董事会对于应对复杂挑战至关重要。良好治理需要深厚信任，很欣慰看到众人携手为OpenAI构建了更强大的治理体系，让我们能继续践行"AGI普惠人类"的使命。

最大收获是意识到需要感恩的人太多：OpenAI全体同仁、助我们度过危机的挚友、支持信任我们的合作伙伴与客户，以及用行动表达关爱的亲友们。[1]

重整旗鼓后我们更专注高效。研究成果达到历史最佳，周活跃用户从1亿增至3亿。最重要的是，我们持续输出真正解决实际问题且广受喜爱的技术。

九年前我们完全不知终将走向何方，即便现在也仅略知一二。AI发展曲折前行，未来仍有更多变数。

有些转折令人欣喜，有些则艰难异常。见证研究奇迹接连涌现、质疑者转为信徒固然美妙，但也目睹同袍成为竞争对手。团队在超常规扩张中必然经历更替——OpenAI的增速让普通公司十年的变化压缩在两年内完成。当企业快速进化时，利益分化在所难免；当行业领军者遭遇竞争，攻击自会接踵而至。

愿景永不改变，策略持续进化。初创时我们未曾预料需要打造产品公司，也不料需要天量资金。未来还会有更多超乎当前想象的挑战。

我们为迄今的研究与落地成果自豪，并将持续深化安全与利益共享的思考。始终相信渐进式发布是确保AI安全的最佳路径，让社会与技术协同进化。我们坚持通过现实应用反馈来引领安全与对齐研究。

如今我们确信掌握传统认知中的AGI构建方法。预计2025年首批AI代理将"加入劳动力大军"，实质性改变企业产出。坚信将强大工具逐步交予民众，终将创造广泛分布的卓越成果。

我们正将目标投向真正的超级智能。现有产品虽好，但辉煌未来才是终极追求。超级智能将极大加速科学发现与创新，远超人类独自所能，进而创造空前繁荣。

这听起来像科幻甚至痴人说梦——但我们早已习惯这种处境。相信未来几年世人终将认同：在追求广泛福祉的同时保持极度谨慎至关重要。鉴于工作性质，OpenAI注定无法成为普通公司。

能参与这项事业何其幸运与谦卑。

(感谢Josh Tyrangiel的访谈启发，唯憾时间仓促)

[1]

危难时刻，许多人付出巨大努力帮助OpenAI与我个人，但有两位尤为突出。

Ron Conway与Brian Chesky的付出远超职责所在。虽久闻Ron的能力与坚韧，也与Brian密切交往多年获益良多，但战壕中的并肩作战才真正展现其非凡之处。

若无他们昼夜不休的斡旋，OpenAI很可能分崩离析。他们始终保持战略清醒，既阻止我犯错，自身也决策完美。动用庞大资源化解复杂局面，还默默完成诸多不为人知的努力。

但最难忘的是那份关怀、共情与支持。

我曾自认理解如何支持创业者与企业，但他们重新定义了这种支持的高度。如今完全明白其传奇地位的由来——虽风格迥异，但都拥有移山填海的非凡能力与患难中的坚定承诺。科技行业因他们而更加美好。

业界还有更多这样的守护者，这种独特文化生态的重要性远超常人认知。我期待将这份善意传递。

最后特别感谢Ollie那个周末乃至始终如一的陪伴，他无可挑剔的伴侣形象令人叹服。

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The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning. New years get people in a reflective mood, and I wanted to share some personal thoughts about how it has gone so far, and some of the things I’ve learned along the way.

As we get closer to AGI, it feels like an important time to look at the progress of our company. There is still so much to understand, still so much we don’t know, and it’s still so early. But we know a lot more than we did when we started.

We started OpenAI almost nine years ago because we believed that AGI was possible, and that it could be the most impactful technology in human history. We wanted to figure out how to build it and make it broadly beneficial; we were excited to try to make our mark on history. Our ambitions were extraordinarily high and so was our belief that the work might benefit society in an equally extraordinary way.

At the time, very few people cared, and if they did, it was mostly because they thought we had no chance of success.

In 2022, OpenAI was a quiet research lab working on something temporarily called “Chat With GPT-3.5”. (We are much better at research than we are at naming things.) We had been watching people use the playground feature of our API and knew that developers were really enjoying talking to the model. We thought building a demo around that experience would show people something important about the future and help us make our models better and safer.

We ended up mercifully calling it ChatGPT instead, and launched it on November 30th of 2022.

We always knew, abstractly, that at some point we would hit a tipping point and the AI revolution would get kicked off. But we didn’t know what the moment would be. To our surprise, it turned out to be this.

The launch of ChatGPT kicked off a growth curve like nothing we have ever seen—in our company, our industry, and the world broadly. We are finally seeing some of the massive upside we have always hoped for from AI, and we can see how much more will come soon.

It hasn’t been easy. The road hasn’t been smooth and the right choices haven’t been obvious.

In the last two years, we had to build an entire company, almost from scratch, around this new technology. There is no way to train people for this except by doing it, and when the technology category is completely new, there is no one at all who can tell you exactly how it should be done.

Building up a company at such high velocity with so little training is a messy process. It’s often two steps forward, one step back (and sometimes, one step forward and two steps back). Mistakes get corrected as you go along, but there aren’t really any handbooks or guideposts when you’re doing original work. Moving at speed in uncharted waters is an incredible experience, but it is also immensely stressful for all the players. Conflicts and misunderstanding abound.

These years have been the most rewarding, fun, best, interesting, exhausting, stressful, and—particularly for the last two—unpleasant years of my life so far. The overwhelming feeling is gratitude; I know that someday I’ll be retired at our ranch watching the plants grow, a little bored, and will think back at how cool it was that I got to do the work I dreamed of since I was a little kid. I try to remember that on any given Friday, when seven things go badly wrong by 1 pm.

A little over a year ago, on one particular Friday, the main thing that had gone wrong that day was that I got fired by surprise on a video call, and then right after we hung up the board published a blog post about it. I was in a hotel room in Las Vegas. It felt, to a degree that is almost impossible to explain, like a dream gone wrong.

Getting fired in public with no warning kicked off a really crazy few hours, and a pretty crazy few days. The “fog of war” was the strangest part. None of us were able to get satisfactory answers about what had happened, or why.

The whole event was, in my opinion, a big failure of governance by well-meaning people, myself included. Looking back, I certainly wish I had done things differently, and I’d like to believe I’m a better, more thoughtful leader today than I was a year ago.

I also learned the importance of a board with diverse viewpoints and broad experience in managing a complex set of challenges. Good governance requires a lot of trust and credibility. I appreciate the way so many people worked together to build a stronger system of governance for OpenAI that enables us to pursue our mission of ensuring that AGI benefits all of humanity.

My biggest takeaway is how much I have to be thankful for and how many people I owe gratitude towards: to everyone who works at OpenAI and has chosen to spend their time and effort going after this dream, to friends who helped us get through the crisis moments, to our partners and customers who supported us and entrusted us to enable their success, and to the people in my life who showed me how much they cared. [1]

We all got back to the work in a more cohesive and positive way and I’m very proud of our focus since then. We have done what is easily some of our best research ever. We grew from about 100 million weekly active users to more than 300 million. Most of all, we have continued to put technology out into the world that people genuinely seem to love and that solves real problems.

Nine years ago, we really had no idea what we were eventually going to become; even now, we only sort of know. AI development has taken many twists and turns and we expect more in the future.

Some of the twists have been joyful; some have been hard. It’s been fun watching a steady stream of research miracles occur, and a lot of naysayers have become true believers. We’ve also seen some colleagues split off and become competitors. Teams tend to turn over as they scale, and OpenAI scales really fast. I think some of this is unavoidable—startups usually see a lot of turnover at each new major level of scale, and at OpenAI numbers go up by orders of magnitude every few months. The last two years have been like a decade at a normal company. When any company grows and evolves so fast, interests naturally diverge. And when any company in an important industry is in the lead, lots of people attack it for all sorts of reasons, especially when they are trying to compete with it.

Our vision won’t change; our tactics will continue to evolve. For example, when we started we had no idea we would have to build a product company; we thought we were just going to do great research. We also had no idea we would need such a crazy amount of capital. There are new things we have to go build now that we didn’t understand a few years ago, and there will be new things in the future we can barely imagine now.

We are proud of our track-record on research and deployment so far, and are committed to continuing to advance our thinking on safety and benefits sharing. We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology safer. We believe in the importance of being world leaders on safety and alignment research, and in guiding that research with feedback from real world applications.

We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.

We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word. We love our current products, but we are here for the glorious future. With superintelligence, we can do anything else. Superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own, and in turn massively increase abundance and prosperity.

This sounds like science fiction right now, and somewhat crazy to even talk about it. That’s alright—we’ve been there before and we’re OK with being there again. We’re pretty confident that in the next few years, everyone will see what we see, and that the need to act with great care, while still maximizing broad benefit and empowerment, is so important. Given the possibilities of our work, OpenAI cannot be a normal company.

How lucky and humbling it is to be able to play a role in this work.

(Thanks to Josh Tyrangiel for sort of prompting this. I wish we had had a lot more time.)

[1]

There were a lot of people who did incredible and gigantic amounts of work to help OpenAI, and me personally, during those few days, but two people stood out from all others.

Ron Conway and Brian Chesky went so far above and beyond the call of duty that I’m not even sure how to describe it. I’ve of course heard stories about Ron’s ability and tenaciousness for years and I’ve spent a lot of time with Brian over the past couple of years getting a huge amount of help and advice.

But there’s nothing quite like being in the foxhole with people to see what they can really do. I am reasonably confident OpenAI would have fallen apart without their help; they worked around the clock for days until things were done.

Although they worked unbelievably hard, they stayed calm and had clear strategic thought and great advice throughout. They stopped me from making several mistakes and made none themselves. They used their vast networks for everything needed and were able to navigate many complex situations. And I’m sure they did a lot of things I don’t know about.

What I will remember most, though, is their care, compassion, and support.

I thought I knew what it looked like to support a founder and a company, and in some small sense I did. But I have never before seen, or even heard of, anything like what these guys did, and now I get more fully why they have the legendary status they do. They are different and both fully deserve their genuinely unique reputations, but they are similar in their remarkable ability to move mountains and help, and in their unwavering commitment in times of need. The tech industry is far better off for having both of them in it.

There are others like them; it is an amazingly special thing about our industry and does much more to make it all work than people realize. I look forward to paying it forward.

On a more personal note, thanks especially to Ollie for his support that weekend and always; he is incredible in every way and no one could ask for a better partner.

GPT-4o || GPT-4o

2025-09-20T21:36:59+00:00

今天公告中有两点想重点强调：

**第一，使命核心：免费提供强大AI工具**  
我们的核心使命是为用户免费（或以极低价格）提供顶尖AI工具。值得骄傲的是，ChatGPT已免费开放全球最佳模型，且无广告干扰。OpenAI创立之初的设想是直接由我们创造AI并产出全球性价值，但如今更可能是我们构建AI基础，而由他人借此创造惊艳成果，最终惠及全人类。作为企业，我们虽会有收费项目，但目标始终是通过商业化支撑向数十亿人提供免费、卓越的AI服务。

**第二，革命性交互：语音/视频模式**  
新版语音（及视频）模式是我用过最惊艳的计算机交互界面，宛如电影中的AI成真——人类级的响应速度与表现力带来质的飞跃。初代ChatGPT展现了语言接口的潜力，而新版本则带来颠覆性体验：快速、智能、有趣、自然且实用。首次让我感到与计算机对话如此自然。未来通过（可选的）个性化设置、信息接入、代理执行等功能，计算机将助力人类实现远超以往的成就。

**最后**，衷心感谢为此付出巨大努力的团队！

---------------

今天的公告中有两点我想特别强调。

首先，我们使命的核心是向人们免费（或以极低价格）提供强大的AI工具。我深感自豪的是，我们已将全球最优秀的模型免费开放于ChatGPT中，且无广告等干扰。

OpenAI创立之初，我们设想的是由我们创造AI并直接为世界创造价值。而现在看来，更可能是我们构建AI后，由他人用它创造出各种令人惊叹的事物，最终惠及所有人。

作为企业，我们当然会开发许多付费服务，但这正是让我们能够为（希望是）数十亿人持续提供免费优质AI服务的基石。

其次，全新的语音（及视频）模式是我用过最出色的计算机交互界面。它宛如电影中的AI成真，至今仍让我感到些许不可思议。达到人类级别的响应速度与表现力，堪称重大突破。

初代ChatGPT展现了语言界面的潜力；而这次的新体验有着本质不同——快速、智能、有趣、自然且实用。

以往与计算机对话总让我觉得不够自然，如今这种感觉彻底改变了。随着（可选的）个性化设置、信息调取权限、代理执行功能等逐步完善，我预见一个激动人心的未来：人类能借助计算机实现前所未有的突破。

最后，衷心感谢为此付出巨大努力的团队！

---------------

There are two things from our announcement today I wanted to highlight.

First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that.

Our initial conception when we started OpenAI was that we’d create AI and use it to create all sorts of benefits for the world. Instead, it now looks like we’ll create AI and then other people will use it to create all sorts of amazing things that we all benefit from.

We are a business and will find plenty of things to charge for, and that will help us provide free, outstanding AI service to (hopefully) billions of people.

Second, the new voice (and video) mode is the best computer interface I’ve ever used. It feels like AI from the movies; and it’s still a bit surprising to me that it’s real. Getting to human-level response times and expressiveness turns out to be a big change.

The original ChatGPT showed a hint of what was possible with language interfaces; this new thing feels viscerally different. It is fast, smart, fun, natural, and helpful.

Talking to a computer has never felt really natural for me; now it does. As we add (optional) personalization, access to your information, the ability to take actions on your behalf, and more, I can really see an exciting future where we are able to use computers to do much more than ever before.

Finally, huge thanks to the team that poured so much work into making this happen!

但愿有人曾告诉我 || What I Wish Someone Had Told Me

2025-09-22T14:04:09+00:00

1. **乐观、痴迷、自信、原始动力与人际关系**是事情起步的关键。  
2. **凝聚力强的团队、冷静与紧迫感的平衡、近乎偏执的投入**是事情完成的要素。长期主义稀缺，别为短期评价焦虑——这会随时间变易。  
3. 团队更易攻克**重要难题**而非无关紧要的简单任务；大胆的想法能激发斗志。  
4. **激励是超能力**，需谨慎设计。  
5. 将资源集中于**少数高确信目标**（说来简单做来难）。你能删减的比想象更多。  
6. **清晰简洁地沟通**。  
7. 随时对抗**形式主义与官僚主义**并号召他人加入。别让组织架构阻碍高效协作。  
8. **结果至上**，别用流程正确掩盖失败。  
9. **加大招聘投入**，敢于押注成长快的高潜力者。除智商外，更看重执行力证据。  
10. **顶尖人才的价值远超表面**，但评估标准应是对组织的净贡献。  
11. **快速迭代能弥补多数错误**。规划以十年计，执行以周为单位。  
12. 别违背商业中的**"物理定律"**。  
13. **灵感易逝，人生短暂**。不作为才是隐蔽的风险。  
14. 规模常伴随**意外涌现的特性**。  
15. **复利效应是魔法**，要打造随规模增长优势叠加的业务。  
16. **跌倒就爬起来继续**。  
17. 与优秀者共事是人生至乐之一。

---------------

乐观、执着、自信、原始动力与人际关系，是事业起步的基石。
团结的团队、冷静与紧迫感的恰当平衡，以及近乎偏执的投入，是成就事业的终点线。长期主义稀缺珍贵，别为短期评价所困——时间会让这变得更容易。
团队完成一件艰难但意义重大的事，比做简单却无关紧要的事更轻松；大胆的想法才能点燃人心。
激励机制如同超能力，务必审慎设计。
将资源集中于少数高确信度的目标上——这道理简单，实践却难。你能删减的冗余远超想象。
沟通要清晰简洁。
每次遇到官僚主义与空谈都要斗争，并号召他人加入。别让组织结构阻碍高效协作。
结果才是一切，别让规范流程成为糟糕表现的借口。
多花时间招募人才。敢于给成长迅猛的高潜力者机会。除了才智，更要看实际成果的证明。
超级明星的价值远超表面，但评估标准应是对组织绩效的净贡献。
快速迭代能弥补许多缺陷——只要迭代够快，犯错通常无妨。规划要以十年计，执行要以周为单位。
不要违背商业世界的"物理定律"。
灵感易逝，人生匆匆。不作为才是最隐蔽的风险。
规模效应常孕育意想不到的涌现特性。
指数级增长如同魔法。尤其要打造能随规模产生复合优势的事业。
跌倒就爬起，继续前进。
与优秀者共事，是人生至乐之一。

---------------

Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started.
Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time.
It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate people.
Incentives are superpowers; set them carefully.
Concentrate your resources on a small number of high-conviction bets; this is easy to say but evidently hard to do. You can delete more stuff than you think.
Communicate clearly and concisely.
Fight bullshit and bureaucracy every time you see it and get other people to fight it too. Do not let the org chart get in the way of people working productively together.
Outcomes are what count; don’t let good process excuse bad results.
Spend more time recruiting. Take risks on high-potential people with a fast rate of improvement. Look for evidence of getting stuff done in addition to intelligence.
Superstars are even more valuable than they seem, but you have to evaluate people on their net impact on the performance of the organization.
Fast iteration can make up for a lot; it’s usually ok to be wrong if you iterate quickly. Plans should be measured in decades, execution should be measured in weeks.
Don’t fight the business equivalent of the laws of physics.
Inspiration is perishable and life goes by fast. Inaction is a particularly insidious type of risk.
Scale often has surprising emergent properties.
Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale.
Get back up and keep going.
Working with great people is one of the best parts of life.

Helion需要你 || Helion Needs You

2025-09-09T13:24:17+00:00

# Helion能源进展与愿景总结

## 技术突破与目标
- **2024年关键里程碑**：  
  1) 实现聚变能量增益Q>1  
  2) 解决量产化聚变发电机的所有技术问题  
- **公司愿景**：  
  - 提供**0.01美元/千瓦时**的清洁持续能源  
  - 十年内量产满足全球当前电力需求的发电厂  

## 潜在影响
- **改变世界**：  
  - 能源**廉价、清洁且无限**将提升全人类生活质量  
  - 低价电能支持高效碳捕集技术，缓解化石燃料依赖  

## 当前挑战
- **需突破的工程难题**：  
  - 设备耐久性设计  
  - 建立**日产量产工厂**与供应链  
  - 全球电网和政府协作  

## 人才需求
**核心岗位招聘**（不完全匹配也鼓励申请）：
1. [低压电气工程师](https://boards.greenhouse.io/helionenergy/jobs/4044506005)  
2. [脉冲电源电气工程师](https://boards.greenhouse.io/helionenergy/jobs/4044510005)  
3. [发电机系统机械工程师](https://boards.greenhouse.io/helionenergy/jobs/4044522005)  
4. [机械工程经理](https://boards.greenhouse.io/helionenergy/jobs/4044521005)  

[所有职位列表](https://www.helionenergy.com/careers/)

---------------

Helion的发展速度甚至超出了我的预期，按照当前进度，2024年将实现两大目标：1）展示Q>1的聚变反应；2）解决设计可大规模生产的聚变发电机所需的所有技术难题。

该公司的目标极具雄心——以每千瓦时1美分的成本提供清洁、持续的能源，并具备在十年内制造满足地球当前电力需求数量的发电厂的能力。

若这两点均能实现，世界将为之改变。充足、清洁且极其廉价的能源将提升全人类的生活质量——想想能源成本对我们日常活动和用品的影响有多大。此外，如此低价的电力还将使我们能够高效实施碳捕获（因此虽然我们暂时仍需依赖汽油，但问题不大）。

尽管Helion过去8年取得的科学进展惊人且必要，但要快速进入这种新能源经济体系仍显不足。Helion现在需要解决如何设计永不故障的机器、如何建立每日量产设备的工厂与供应链、如何与全球电网和政府合作等诸多工程挑战。

当前决定公司成功程度与速度的最大因素，就是加入团队的人才素质。以下是几个最关键职位，但请勿因不完全匹配而放弃申请。

低压电气工程师：https://boards.greenhouse.io/helionenergy/jobs/4044506005
脉冲电源电气工程师：https://boards.greenhouse.io/helionenergy/jobs/4044510005
发电机系统机械工程师：https://boards.greenhouse.io/helionenergy/jobs/4044522005
机械工程经理：http s://boards.greenhouse.io/helionenergy/jobs/4044521005

（所有在招职位：https://www.helionenergy.com/careers/)

---------------

Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator.

The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period.

If both things happen, it will transform the world. Abundant, clean, and radically inexpensive energy will elevate the quality of life for all of us—think about how much the cost of energy factors into what we do and use. Also, electricity at this price will allow us to do things like efficiently capture carbon (so although we’ll still rely on gasoline for awhile, it’ll be ok).

Although Helion’s scientific progress of the past 8 years is phenomenal and necessary, it is not sufficient to rapidly get to this new energy economy. Helion now needs to figure out how to engineer machines that don’t break, how to build a factory and supply chain capable of manufacturing a machine every day, how to work with power grids and governments around the world, and more.

The biggest input to the degree and speed of success at the company is now the talent of the people who join the team. Here are a few of the most critical jobs, but please don’t let the lack of a perfect fit deter you from applying.

Electrical Engineer, Low Voltage: https://boards.greenhouse.io/helionenergy/jobs/4044506005
Electrical Engineer, Pulsed Power: https://boards.greenhouse.io/helionenergy/jobs/4044510005
Mechanical Engineer, Generator Systems: https://boards.greenhouse.io/helionenergy/jobs/4044522005
Manager of Mechanical Engineering: http s://boards.greenhouse.io/helionenergy/jobs/4044521005

(All current jobs: https://www.helionenergy.com/careers/)

DALL•E 2 || DALL•E 2

2025-09-11T01:26:21+00:00

今天我们发布了DALL•E 2的研究版本，这是一款能通过自然语言指令生成和编辑图像的新AI工具。最重要的，我们希望人们喜欢并发现它的价值。对我而言，这是迄今为止我们创造的最令人愉悦的作品——它能激发创造力、适用于多元场景，并带来久违的科技趣味性。但它的意义远不止于此：

1. **自然语言交互的新趋势**  
   这印证了计算机交互的新方向：用自然语言或上下文线索表达需求，计算机即可执行。我们从代码生成延伸到图像创作，未来这两者都会大幅进化，最终将能处理复杂任务——比如像人类一样理解自然语言的"AI办公室职员"。

2. **多层次概念理解能力**  
   系统展现出对复杂概念及其关联性的深度"理解"。

3. **对劳动力市场的潜在影响**  
   不同于仅提升效率的编程助手Copilot，DALL•E 2能直接产出"完整作品"。这可能是AI冲击就业市场的早期信号：虽然AI会创造新岗位并优化现有工作，但某些职业的不可替代性将逐渐降低。

4. **颠覆AI发展预测**  
   十年前普遍认为AI会先影响体力劳动，再是认知劳动，最后才可能涉足创意领域。现实却正以相反顺序上演。

5. **创意即上限的新纪元**  
   在这个世界里，限制我们的是好点子，而非专业技能。

6. **强大能力的双刃剑**  
   尽管前景广阔，但必须正视潜在风险。我们选择先发布研究版本，正是为了联合更多研究者和艺术家共同探索风险管控，同时给社会适应变革的时间——我们始终相信渐进式部署策略。（当然，世界早已拥有Photoshop，我们都清楚图像编辑技术的双面性。）

（配图说明：DALL•E绘制的机械手素描）

注：译文采用模块化排版突出核心观点，保留技术术语统一性（如"自然语言/Copilot"），将口语化表达转化为简洁的书面语，并通过分段标题增强可读性。最后配图说明以括号标注与原风格一致。

---------------

今天我们发布了DALL•E 2的研究版本，这是一款能根据自然语言指令生成和编辑图像的新AI工具。

最重要的是，我们希望人们喜爱并发现它的实用价值。对我而言，这是我们迄今创造的最令人愉悦的作品。它能激发创造力，适用于多种场景，其带来的乐趣是我许久未从科技产品中感受到的。

但它的意义还体现在以下几个方面：

1) 这再次印证了我预见的计算机交互新趋势：用自然语言或情境线索表达需求，计算机就能执行。我们已在代码领域实现这一点，现在拓展到图像生成；两者都将大幅进化。这种趋势将持续渗透，终将胜任复杂任务——我们可以想象未来会出现能像人类一样理解自然语言指令的"AI办公室职员"。

2) 它确实能以多层次"理解"概念，并把握其间的精妙关联。

3) Copilot是提升程序员效率的工具，但远不能独立开发完整程序。DALL•E 2既能辅助艺术家激发灵感，也能独立完成"完整作品"。这可能是AI影响劳动力市场的早期例证。虽然我坚信AI将创造大量新岗位，并通过高效处理枯燥工作提升现有职业价值，但必须承认某些岗位将逐渐边缘化（技术革新往往如此）。

4) 这提醒我们：AI预测极具挑战。十年前普遍认为AI会先影响体力劳动，再波及脑力劳动，最后才可能涉足创意领域。如今发展轨迹似乎正相反。

5) 它昭示着：未来限制我们的是创意质量，而非特定技能。

6) 尽管优势显著，但该模型的强大能力也容易引发负面想象。

今年夏天我们计划推出正式产品版，届时人们可将其用于各种场景。选择先发布研究版本，是为了与更多研究者和艺术家合作探索如何最小化风险，也给社会适应变革留出时间——我们始终信奉渐进式部署策略。（当然，世界已有Photoshop，图像篡改的利弊我们都已知晓）

(DALL•E绘制的机械臂素描)

---------------

Today we did a research launch of DALL•E 2, a new AI tool that can create and edit images from natural language instructions.

Most importantly, we hope people love the tool and find it useful. For me, it’s the most delightful thing to play with we’ve created so far. I find it to be creativity-enhancing, helpful for many different situations, and fun in a way I haven’t felt from technology in a while.

But I also think it’s noteworthy for a few reasons:

1) This is another example of what I think is going to be a new computer interface trend: you say what you want in natural language or with contextual clues, and the computer does it. We offer this for code and now image generation; both of these will get a lot better. But the same trend will happen in new ways until eventually it works for complex tasks—we can imagine an “AI office worker” that takes requests in natural language like a human does.

2) It sure does seem to “understand” concepts at many levels and how they relate to each other in sophisticated ways.

3) Copilot is a tool that helps coders be more productive, but still is very far from being able to create a full program. DALL•E 2 is a tool that will help artists and illustrators be more creative, but it can also create a “complete work”. This may be an early example of the impact AI on labor markets. Although I firmly believe AI will create lots of new jobs, and make many existing jobs much better by doing the boring bits well, I think it’s important to be honest that it’s increasingly going to make some jobs not very relevant (like technology frequently does).

4) It’s a reminder that predictions about AI are very difficult to make. A decade ago, the conventional wisdom was that AI would first impact physical labor, and then cognitive labor, and then maybe someday it could do creative work. It now looks like it’s going to go in the opposite order.

5) It’s an example of a world in which good ideas are the limit for what we can do, not specific skills.

6) Although the upsides are great, the model is powerful enough that it's easy to imagine the downsides.

Hopefully this summer, we’ll do a product launch and people will be able to use it for all sorts of things. We wanted to start with a research launch to figure out how to minimize the downsides in collaboration with a larger group of researchers and artists, and to give people some time to adapt to the change—in general, we are believers in incremental deployment strategies. (Obviously the world already has Photoshop and we already know that images can be manipulated, for good and bad.)

(A robot hand drawing, by DALL•E)

赫利昂 || Helion

2025-09-09T13:24:29+00:00

# 投资Helion的喜悦

我很高兴能对Helion进行追加投资。迄今为止，这是我见过最有前景的核聚变技术方案。

## 非凡的创始团队
- David和Chris是我见过最令人印象深刻的创始人兼建造者（既建造公司，又建造核聚变装置！）
- 他们取得了惊人成就：用其他核聚变项目零头的资金+初创企业文化，就建成了能发电的装置

## 技术突破
- 2014年首次投资时就被打动：科学方法、系统设计、成本优化和燃料循环的周密规划
- 明确路线：2024年实现净发电，长期目标度电成本1美分/千瓦时

## 变革性潜力
1. **气候危机解决方案**  
   - 即使非电力排放也存在，充足能源可支持碳捕集
2. **能源革命**  
   - 超低成本能源将根本性降低其他领域成本
   - 显著提升全球生活质量
3. **想象空间**  
   - 廉价能源将催生现今难以想象的创新

（注：原文中"(!)"表情符号为保留作者强调语气）

这个版本：

采用模块化结构突出核心信息
保留原文关键数据和时间节点
通过列表形式提升可读性
使用中文惯用表达（如"零头资金"）
保持技术术语准确性（如"净发电"）
最后备注处理了原文的特殊符号

---------------

我非常高兴能对Helion进行更多投资。Helion无疑是我所见过的核聚变领域最具前景的技术路径。

David和Chris是我见过最令人印象深刻的创始人与建造者（不仅是创建公司，更是在建造核聚变装置！），他们取得了非凡成就。2014年我第一次投资他们时，就被他们在科学方法、系统设计、成本优化和燃料循环方面的周密计划所震撼。

如今，他们及其团队仅以其他核聚变项目零头的资金投入，凭借初创企业的文化精神，成功建造出能发电的装置。Helion已明确规划在2024年前实现净电力输出，并设定了长期目标——将发电成本降至每千瓦时1美分。（！）

如果一切如我们所愿，我们或许能找到摆脱气候危机的出路。尽管并非所有排放都来自发电领域，但我们将能利用充沛的能源来捕获二氧化碳和其他温室气体。

若能获得前所未有的廉价能源，我们将实现今日难以想象的突破。能源成本是影响万物价格的基础要素之一；能源价格的大幅下降将显著提升无数人的生活品质。

---------------

I’m delighted to be investing more in Helion. Helion is by far the most promising approach to fusion I’ve seen.

David and Chris are two of the most impressive founders and builders (in the sense of building fusion machines, in addition to building companies!) I have ever met, and they have done something remarkable. When I first invested in them back in 2014, I was struck by the thoughtfulness of their plans about the scientific approach, the system design, cost optimizations, and the fuel cycle.

And now, with a tiny fraction of the money spent on other fusion efforts but the culture of a startup, they and their team have built a generator that produces electricity. Helion has a clear path to net electricity by 2024, and has a long-term goal of delivering electricity for 1 cent per kilowatt-hour. (!)

If this all works as we hope, we may have a path out of the climate crisis. Even though there are a lot of emissions that don’t come from electrical generation, we’d be able to use abundant energy to capture carbon and other greenhouses gases.

And if we have much cheaper energy than ever before, we can do things that are difficult to imagine today. The cost of energy is one of the fundamental inputs in the costs of so much else; dramatically cheaper energy will lead to dramatically better quality of life for many people.

被误解的力量 || The Strength of Being Misunderstood

2025-09-21T22:02:04+00:00

一位创始人曾问我："如何停止在意他人的看法？"我当时没有答案，但经过思考后，我认为这是个错误的问题。几乎所有人都会在意某些人的评价（尽管在意所有人的想法绝对是错误的），而这种在意可能很重要。

**关键在于两个维度：**
1. **选择你在意的人**（及具体领域）  
   - 过度在意会让你成为盲从的绵羊  
   - 但完全不在意他人就无法做出对他人有价值的事  
   - *正如香奈儿所说："我不在乎你对我的看法，因为我根本就没想过你"*

2. **选择你在意的时间尺度**  
   - 我认识的那些卓越者往往异常在意他人评价（甚至包括不该在意的人，比如有人会专门收藏黑粉的推文截图）  
   - 但他们的特别之处在于：**用长期主义看待评价**  
   - 他们追求的是"历史书上的正确"，而非"报纸上的认同"  

**核心策略：**  
- 用短期低社会地位换取长期高社会地位（多数人拒绝这种交换）  
- 当你的判断正确时，被大众误解反而是优势——这让你和小众叛逆者能专注解决那些本该被忽视的重要问题  

**终极心法：**  
在重要但非共识的领域坚持正确，比获得当下认可更有价值。那些改变世界的人，往往最初都被认为是疯子。

---------------

最近有位创始人问我，如何做到不在意他人的看法。我当时没有答案，经过更深入的思考后，我认为这是个错误的问题。

几乎所有人都会在意某些人的看法（尽管在意所有人的看法绝对是错误的），这或许很重要。过分在意会让你成为羊群中的一员。但为了能为他人做些有用的事，你至少需要与他人保持一定程度的共鸣。

这似乎涉及两个自由度：你可以选择在意哪些人的意见（以及在哪些主题上），也可以选择在意这些意见的时间尺度。多数人能处理好前者[1]，但后者似乎很少被关注。

我认识的那些令人印象深刻的人，往往非常在意他人的看法，甚至包括那些他们本不该重视的人（令人惊讶的是，他们中不少人会做类似保存仇恨者推文截图的事情）。但他们与众不同的是，通常以极长的时间跨度来考量他人意见——只要历史书能给出正确评价，他们甚至会为报纸的误报感到些许自豪。

你应该用短期的低地位换取长期的高地位，而这似乎是多数人不愿做的。常见的方式是通过最终在一个重要但极度非共识的赌注上证明自己是对的。但还有许多其他方法——关键在于，只要你是正确的，被大多数人误解不是弱点而是优势。你和一小群反叛者因此获得了解决重要问题的空间，而这些问题可能原本无人问津。

[1] 用可可·香奈儿那句令人难忘的话来说："我不在乎你对我的看法。我根本就没考虑过你。"

---------------

A founder recently asked me how to stop caring what other people think. I didn’t have an answer, and after reflecting on it more, I think it's the wrong question.

Almost everyone cares what someone thinks (though caring what everyone thinks is definitely a mistake), and it's probably important. Caring too much makes you a sheep. But you need to be at least a little in tune with others to do something useful for them.

It seems like there are two degrees of freedom: you can choose the people whose opinions you care about (and on what subjects), and you can choose the timescale you care about them on. Most people figure out the former [1] but the latter doesn’t seem to get much attention.

The most impressive people I know care a lot about what people think, even people whose opinions they really shouldn’t value (a surprising numbers of them do something like keeping a folder of screenshots of tweets from haters). But what makes them unusual is that they generally care about other people’s opinions on a very long time horizon—as long as the history books get it right, they take some pride in letting the newspapers get it wrong.

You should trade being short-term low-status for being long-term high-status, which most people seem unwilling to do. A common way this happens is by eventually being right about an important but deeply non-consensus bet. But there are lots of other ways–the key observation is that as long as you are right, being misunderstood by most people is a strength not a weakness. You and a small group of rebels get the space to solve an important problem that might otherwise not get solved.

[1] In the memorable words of Coco Chanel, “I don’t care what you think about me. I don’t think about you at all.”

PG与杰西卡 || PG and Jessica

2025-09-07T06:01:58+00:00

许多人试图在其他行业、地区或用不同策略复制YC（Y Combinator）的成功。普遍存在两种假设：  
1) 尽管YC起步时带有一定神秘性或运气成分，但复制应该不难；  
2) 一旦启动，网络效应会自然维持。  

更多类似YC的尝试对世界有益，我通常也愿意提供帮助。但几乎所有这些尝试都失败了。人们关于"自我维持"的判断是对的，却无法解决从零到一的问题。**YC成功的全部秘密在于保罗·格雷厄姆（PG）和杰西卡（Jessica）——没有其他魔法。**  

每年我总会在聚会上听到这样的故事：某人讲述PG如何改变他们的人生，言语中的感激远超对其他任何人。非YC创始人可能提到PG的文章启发或在YC公司工作的经历。遗憾的是，杰西卡公开获得的赞誉远不及PG，但YC早期成员知道真相。  

**他们的核心做法：**  
- **押注无名之辈**：比任何人更相信这些人的潜力；  
- **建立强规范**：坚决抵制对YC创始人的不公行为；  
- **坚持自我**：不惧与传统权力结构对立；  
- **极致专注**：投入大量时间与创始人1对1沟通；  
- **生态思维**（乔·吉比亚指出）：甘愿"少赚钱"，让合作伙伴、校友、投资者等整个生态受益——这恰恰巩固了YC的地位。  

这些听起来不难，但15年来无人接近复制。或许最关键的是：**他们对初创行业从业者的影响力，至今无人能及。**

（注：根据要求，保留了英文缩写"YC"和"PG"，其他专有名词如"Jessica"未翻译以保持一致性；关键观点通过加粗和列表突出显示；原文中的人名、平台名等专有名词未意译。）

---------------

许多人试图在其他行业、地域或采用不同策略复制YC的成功。普遍存在两种假设：1) 虽然YC起步时带有某种神秘色彩或运气成分，但复制起来应该不难；2) 只要能启动项目，网络效应就会自然形成。

更多类似YC的机构对世界有益，我也常乐于提供帮助。但几乎所有的尝试都失败了。人们关于网络效应自我维持的判断没错，却始终无法找到启动的关键。

YC成功的全部秘诀就在于保罗·格雷厄姆和杰西卡——除此之外别无魔法。每年我总会在聚会上听到这样的故事：某人讲述保罗如何改变他们的人生——那种感激之情远超对其他任何人。在场所有人都会附和，无论是YC创始人还是局外人（非YC创始人可能会提到某篇影响深远的文章或在YC公司获得工作机会）。遗憾的是杰西卡至今未获得同等程度的公众认可，但见证YC早期岁月的人都明白真相。

他们做了什么？他们押注无名之辈，给予前所未有的信任。他们建立严格准则，坚决抵制针对YC创始人的不良行为。他们坚持己见，敢于特立独行，甘愿得罪现有权力结构。他们聚焦最重要的事，勤奋工作，投入大量时间进行一对一交流。他们深谙社群价值和长期主义。当YC还很小时，它就像一个大家庭。

或许最重要的是，他们构建了一个生态系统（感谢乔·吉比亚指出这点）。这说起来容易做起来难，因为需要克制贪婪。YC放弃了许多唾手可得的收益，生态系统中其他人赚的钱比YC本身还多。这巩固了YC的地位——合作伙伴、校友、当期创始人、Hacker News读者、演示日投资者以及所有YC周边人群获得的利益，正是其成功运转的核心要素。

我不确定这些是否算特别有用的建议——听起来都不算困难，但十五年来始终无人能接近复制。

但这值得尝试。我确信在这段时间里，没有谁比他们两人对初创行业从业者的职业生涯产生过更深远的影响。

---------------

A lot of people want to replicate YC in some other industry or some other place or with some other strategy. In general, people seem to assume that: 1) although there was some degree of mystery or luck about how YC got going, it can’t be that hard, and 2) if you can get it off the ground, the network effects are self-sustaining.

More YC-like things are good for the world; I generally try to be helpful. But almost none of them work. People are right about the self-sustaining part, but they can’t figure out how to get something going.

The entire secret to YC getting going was PG and Jessica—there was no other magic trick. A few times a year, I end up in a conversation at a party where someone tells a story about how much PG changed their life—people speak with more gratitude than they do towards pretty much anyone else. Then everyone else agrees, YC founders and otherwise (non-YC founders might talk about an impactful essay or getting hired at a YC company). Jessica still sadly doesn’t get nearly the same degree of public credit, but the people who were around the early days of YC know the real story.

What did they do? They took bets on unknown people and believed in them more than anyone had before. They set strong norms and fought back hard against bad behavior towards YC founders. They trusted their own convictions, were willing to do things their way, and were willing to be disliked by the existing power structures. They focused on the most important things, they worked hard, and they spent a huge amount of time 1:1 with people. They understood the value of community and long-term orientation. When YC was very small, it felt like a family.

Perhaps most importantly, they built an ecosystem (thanks to Joe Gebbia for pointing this out). This is easy to talk about but hard to do, because it requires not being greedy. YC has left a lot of money on the table; other people have made more money from the ecosystem than YC has itself. This has cemented YC’s place—the benefits to the partners, alumni, current batch founders, Hacker News readers, Demo Day investors, and everyone else around YC is a huge part of what makes it work.

I am not sure if any of this is particularly useful advice—none of it sounds that hard, and yet in the 15 years since, it hasn’t been close to replicated.

But it seems worth trying. I am pretty sure no one has had a bigger total impact on the careers of people in the startup industry over that time period than the two of them.

研究者与创始人 || Researchers and Founders

2025-09-09T12:14:49+00:00

我曾与创业者共事多年，如今则与科研工作者合作。尽管个体总有例外，但这两类顶尖人才的平均差异令我惊讶（包括某些我曾以为所有优秀人才都具备的特质，比如极强的自信）。因此我开始思考他们的共性，或许能从中提炼出高效能人士的普遍特质。

**核心共性：**  
1. **问题意识**  
   两类顶尖人才都频繁思考"汉明问题"的变体——"你所在领域最关键的问题是什么？为何你还没攻克它们？"  
   - 多数人思考不足，但顶尖者不仅思考最多，还拥有最佳的"问题品味"  
   - 这种品味=独立思维+未来推演+突破口识别（推荐阅读[John Schulman的见解](http://joschu.net/blog/opinionated-guide-ml-research.html)）

2. **双重视角**  
   - 激光般的短期执行聚焦 × 清晰的长期愿景  
   - 多数人仅具备其一

3. **持续行动力**  
   - 极端坚韧且愿意付出努力  
   - 警惕那些宣称"无需努力就能成功"的人（尤其当说教者自身就是奋斗者时）

4. **实验精神**  
   - 行动导向：快速试错  
   - 清醒认知：客观评估成效（令人惊讶的是，很多人看到有效路径却不去跟进）

5. **创造力喷泉**  
   - 虽可能产生大量糟糕点子，但永不枯竭

6. **规则质疑者**  
   - 高度珍视自主权  
   - 难以忍受自认不合理的规则  
   - 绝非盲从者

7. **深层驱动力**  
   - 动机常比表面更复杂  
   - 核心驱动力往往是纯粹的好奇心

---------------

我曾与创业者共事多年，如今则与研究人员合作。

尽管总有个体差异，但平均而言，这两类顶尖人才之间的差异之大令我惊讶（包括某些我曾以为所有优秀人才都具备的特质，比如极强的自我信念）。

因此我开始思考他们的共同点，或许这能帮助我们理解高效能人士的普遍特质。

两类顶尖人才都会花大量时间思考某个版本的"汉明问题"——"你所在领域最重要的问题是什么？为什么你没有研究它们？"总体而言，人们对这个问题的思考永远不够，但最优秀的人思考得最多，并拥有最佳的"问题品味"，这需要结合独立思考能力、未来推演能力和关键突破口识别能力。（John Schulman的这篇文章值得一读：http://joschu.net/blog/opinionated-guide-ml-research.html）

他们既能像激光般聚焦眼前步骤，又具备长远视野。多数人只能做到其中一点。

他们极度坚韧且愿意付出努力。据我观察，若没有这种特质，几乎不可能大概率获得巨大成功。对于那些声称可以不劳而获的人，除非你甘愿复制他们的职业生涯，否则应当保持怀疑（尤其当这些人自己曾拼命工作时更应警惕）。

他们倾向于行动优先、勇于尝试，并能清醒诚实地评估哪些方法有效、哪些无效（值得注意的是，这种特质是双向的——我惊讶于许多人看到有效方法后竟不去跟进）。

他们是创意的永动机——虽然很多想法可能很糟糕，但从不缺乏新点子。

他们极度重视自主权，难以接受自认为不合理的规则。他们绝非盲从之辈。

其动机往往比表面看起来更复杂——具体而言，他们常被纯粹的好奇心强烈驱动。

---------------

I spent many years working with founders and now I work with researchers.

Although there are always individual exceptions, on average it’s surprising to me how different the best people in these groups are (including in some qualities that I had assumed were present in great people everywhere, like very high levels of self-belief).

So I’ve been thinking about the ways they’re the same, because maybe there is something to learn about qualities of really effective people in general.

The best people in both groups spend a lot of time reflecting on some version of the Hamming question—"what are the most important problems in your field, and why aren’t you working on them?” In general, no one reflects on this question enough, but the best people do it the most, and have the best ‘problem taste’, which is some combination of learning to think independently, reason about the future, and identify attack vectors. (This from John Schulman is worth reading: http://joschu.net/blog/opinionated-guide-ml-research.html).

They have a laser focus on the next step in front of them combined with long-term vision. Most people only have one or the other.

They are extremely persistent and willing to work hard. As far as I can tell, there is no high-probability way to be very successful without this, and you should be suspicious of people who tell you otherwise unless you’d be happy having their career (and be especially suspicious if they worked hard themselves).

They have a bias towards action and trying things, and they’re clear-eyed and honest about what is working and what isn’t (importantly, this goes both ways—I’m amazed by how many people will see something working and then not pursue it).

They are creative idea-generators—a lot of the ideas may be terrible, but there is never a shortage.

They really value autonomy and have a hard time with rules that they don’t think make sense. They are definitely not lemmings.

Their motivations are often more complex than they seem—specifically, they are frequently very driven by genuine curiosity.

项目共价键 || Project Covalence

2025-09-22T02:25:30+00:00

# 项目概述：提升COVID-19临床试验效率的协作计划  

几乎所有我接触的从事COVID-19研究的公司和非营利组织都提出需要**临床试验支持**——对于开发新药、疫苗和诊断技术的企业而言，快速启动临床试验是最大的瓶颈之一。科学仍是解决COVID-19危机的唯一途径，而显著改进传统耗时耗资（数千万至数亿美元）的临床试验，是加速突破的关键。  

## 项目目标  
本项目与**TrialSpark**及**Mark Fishman博士**合作，旨在为COVID-19研究提供远超现有水平的临床试验支持。通过**Project Covalence**平台（由TrialSpark驱动），我们专门优化了针对COVID-19试验的方案：  

- **试验场景创新**：优先在社区或患者家中开展，减轻医院和医疗系统负担。  
- **全流程支持**：覆盖试验执行、远程合规数据采集（符合21 CFR Part 11标准）、远程医疗、生物统计、居家样本采集套件及试验方案撰写。  
- **共享基础设施**：学术界和产业界的研究人员可利用该平台快速启动试验。  

## 协同设计  
为促进研究间协作，我们将制定**主协议框架**，支持：  
- 共享对照组  
- 适应性试验设计  

## 参与方式  
如果您有兴趣参与或有试验需支持，请联系：  
📧 ProjectCovalence@trialspark.com  
🌐 [www.projectcovalence.com](http://www.projectcovalence.com)

---------------

几乎所有我提出愿意提供帮助的、致力于COVID-19研究的公司和非营利组织，都请求在临床试验方面获得支持——对于那些专注于开发新药、疫苗和诊断技术的企业而言，快速启动试验是他们面临的最大瓶颈之一。

科学仍是摆脱COVID-19危机的唯一途径。大幅改进通常耗时且耗资数千万至数亿美元的临床试验，是加速摆脱危机的最高效方式之一。

该项目与TrialSpark及Mark Fishman博士合作，旨在为COVID-19相关研究提供远超现有水平的临床试验支持。

由TrialSpark提供技术支持的Project Covalence平台，专为优化COVID-19试验而设计。这类试验最理想的是在社区环境或患者家中进行，以减轻医院和医疗系统的负担。Project Covalence具备独特优势，能够应对启动此类试验所涉及的操作与物流挑战，支持试验执行、符合21 CFR第11部分的远程数据收集、远程医疗、生物统计学、居家样本采集工具包以及试验方案撰写。

学术界和工业界的研究人员均可利用这一共享基础设施快速启动临床试验。为促进研究间的协调，我们还将为平台研究制定主协议，以实现共享对照组和适应性试验设计。

如果您有兴趣参与或有需要支持的试验，请联系ProjectCovalence@trialspark.com或访问www.projectcovalence.com。

---------------

Almost every company and non-profit working on COVID-19 that I offered to help asked for support with clinical trials—for companies focusing on developing novel drugs, vaccines, and diagnostics, rapidly spinning up trials is one of their biggest bottlenecks.

Science remains the only way out of the COVID-19 crisis. Dramatically improving clinical trials, which are usually time-consuming and cost tens to hundreds of millions of dollars, is one of the highest-leverage ways to get out of it faster.

The goal of this project, in collaboration with TrialSpark and Dr. Mark Fishman, is to offer much better clinical trial support to COVID-19 projects than anything that currently exists.

Project Covalence’s platform, powered by TrialSpark, is uniquely optimized to support COVID-19 trials, which are ideally run in community settings or at the patient’s home to reduce the burden placed on hospitals and health systems. Project Covalence is well-positioned to tackle the operational and logistical challenges involved in launching such trials, and supports trial execution, 21 CFR Part 11 compliant remote data collection, telemedicine, biostatistics, sample kits for at-home specimen collection, and protocol writing.

Researchers across academia and industry can leverage this shared infrastructure to rapidly launch their clinical trials. To facilitate coordination between studies, we will also be creating master protocols for platform studies to enable shared control arms and adaptive trial designs.

If you’re interested in getting involved or have a trial that needs support, please get in touch at ProjectCovalence@trialspark.com or visit www.projectcovalence.com.

创意生成 || Idea Generation

2025-09-10T00:52:48+00:00

# 如何获得创业灵感：给未来创始人的建议

## 核心观点
1. **创意能力是创始人的核心素质**  
   - 单纯给创始人提供创意几乎总是失败
   - YC曾尝试资助"无创意"的优秀创始人，结果全部失败
   - 优秀创始人往往对万事万物都有大量想法

## 培养创意的环境要素
2. **选择正确的环境与人脉**  
   - 寻找具备这些特质的人群：
     ✓ 对未来有敏锐感知  
     ✓ 愿意探讨看似不可能的计划  
     ✓ 保持乐观态度  
     ✓ 具备创造性智慧  
     ✓ 高频产出创意  
   - 远离两类人：
     ✗ 对世界充满倦怠感的人  
     ✗ 贬低你抱负的人  

## 创意思维方法论
3. **前瞻性思考技巧**  
   - 采用"未来回溯法"：想象20年后的世界，逆向思考发展路径
   - 关注正在发生的结构性变革：
     • 2008-2012年智能手机爆发就是典型案例
     • 辨别真伪趋势的关键：观察新平台是"少数人高频使用"还是"多数人低频使用"

4. **机会捕捉策略**  
   - 特别关注"去年不可行，今年变可能"的领域
   - 最佳信号："我确信这会发生，只是不确定是否由我们实现"（如Uber案例）

## 创意评估标准
5. **判断创意的潜力**  
   - 关键问题："如果成功，这个想法能否变得巨大？"
   - 寻找具有累积优势的商业模式（如Facebook、Airbnb）
   - 好的创意往往具备：大众认为糟糕但你看到其价值的特征

6. **创始人适配度**  
   - 创始人/公司匹配度与产品/市场匹配度同等重要
   - 通过导师或同事反馈了解自身独特优势

> 本文改编自2018年YC中国活动的演讲笔记，作者从专注创业思考转向AI发展领域后，发现第5-9段内容仍具高度适用性。

---------------

准创业者们最常问的问题是如何获得创业点子。紧随其后的问题是：你有没有什么创业点子可以给他们。

但直接给创始人一个点子几乎总是行不通。拥有创意能力是创业者最重要的素质之一——在运营初创企业的过程中，你将需要源源不断地产生新想法。

YC曾尝试过一个实验：资助那些看起来优秀但没有具体想法的创始人。我认为这个"无创意赛道"上的每一家公司都失败了。事实证明，优秀的创始人会对万事万物产生大量想法。因此，若你想成为创始人却苦于没有创业点子，或许应该先提升自己的创意生成能力。

如何做到这一点？

关键在于置身于合适的环境，与合适的人群为伍。你需要接触那些具有未来洞察力、愿意探讨看似不可能的计划、保持乐观态度、拥有创造性智慧且思维极其活跃的人。这类人往往能突破常人思维的局限，少有思想束缚，也不太在意他人看法。

最好的创意往往很脆弱——大多数人甚至不会开口讨论，因为这些想法听起来很愚蠢。最重要的是，你需要与那些不会因你提出糟糕点子而让你感到难堪的人相处，他们自己也从不为此感到尴尬。

远离那些世故圆滑、轻视你抱负的人。遗憾的是，这世上大多数人都如此。但他们固守过去，而你应当面向未来。

试着将自己投射到20年后的未来，然后逆向思考。相信自己——20年足够漫长，即便你对未来的构想显得激进也无可厚非。

另一种方法是思考当前最重要的结构性变革。世界正在发生哪些根本性变化？你能否识别出变革的前沿及其带来的机遇？2008-2012年的智能手机爆发就是最近的典型案例——我们早该迎来下一次革命了！

在这类结构性变革中，世界变化如此之快，以至于行业巨头往往会被行动敏捷、专注的初创企业击败。（顺便说，学会区分真实趋势与虚假趋势很有用。关键区别在于：新平台是被少数人频繁使用，还是被多数人偶尔使用。）

任何时候，当你发现某件事今年可行而去年不可行时，都值得密切关注。这可能孕育着伟大创业想法的种子。特别是当这个机遇窗口可能明年就会关闭时。

当你能说出"我确信这一定会发生，只是不确定是否由我们来实现"时，这就是个好兆头。优步（Uber）对我来说就是如此——首次使用后，我就明白电话叫车终将式微，只是不确定优步能否胜出。

在构思创意的早期阶段，不妨自问："如果成功，这个想法能否产生巨大影响？"世界上有许多好点子，但极少具备使初创企业取得巨大成功的内在优势。大多数业务在规模扩张时无法形成有价值的累积优势。要尽早思考某个创意为何可能具备这种特质。对Facebook或Airbnb来说这显而易见，但更多时候这种优势表现得更为微妙。

思考自身优势同样重要。仅靠自我反省很难准确判断，理想情况下可以请教导师或共事过的同事。我逐渐相信，创始人与其公司的契合度，与产品市场契合度同等重要。

最后，检验创意的一个好方法是：你能否清楚解释为什么多数人认为这是个糟糕主意，而你却明白其价值所在。

本文摘自我2018年在YC中国活动上的演讲笔记。感谢Eric Migicovsky鼓励我发布这些内容！

撰写时我主要关注初创企业；如今我更专注于AI发展。惊讶地发现其中大部分内容依然适用，特别是第5-9段。

---------------

The most common question prospective startup founders ask is how to get ideas for startups. The second most common question is if you have any ideas for their startup.

But giving founders an idea almost always doesn’t work. Having ideas is among the most important qualities for a startup founder to have—you will need to generate lots of new ideas in the course of running a startup.

YC once tried an experiment of funding seemingly good founders with no ideas. I think every company in this no-idea track failed. It turns out that good founders have lots of ideas about everything, so if you want to be a founder and can’t get an idea for a company, you should probably work on getting good at idea generation first.

How do you do that?

It’s important to be in the right kind of environment, and around the right kind of people. You want to be around people who have a good feel for the future, will entertain improbable plans, are optimistic, are smart in a creative way, and have a very high idea flux. These sorts of people tend to think without the constraints most people have, not have a lot of filters, and not care too much what other people think.

The best ideas are fragile; most people don’t even start talking about them at all because they sound silly. Perhaps most of all, you want to be around people who don’t make you feel stupid for mentioning a bad idea, and who certainly never feel stupid for doing so themselves.

Stay away from people who are world-weary and belittle your ambitions. Unfortunately, this is most of the world. But they hold on to the past, and you want to live in the future.

You want to be able to project yourself 20 years into the future, and then think backwards from there. Trust yourself—20 years is a long time; it’s ok if your ideas about it seem pretty radical.

Another way to do this is to think about the most important tectonic shifts happening right now. How is the world changing in fundamental ways? Can you identify a leading edge of change and an opportunity that it unlocks? The mobile phone explosion from 2008-2012 is the most recent significant example of this—we are overdue for another!

In such a tectonic shift, the world changes so fast that the big incumbents usually get beaten by fast-moving and focused startups. (By the way, it’s useful to get good at differentiating between real trends and fake trends. A key differentiator is if the new platform is used a lot by a small number of people, or used a little by a lot of people.)

Any time you can think of something that is possible this year and wasn’t possible last year, you should pay attention. You may have the seed of a great startup idea. This is especially true if next year will be too late.

When you can say “I am sure this is going to happen, I’m just not sure if we’ll be the ones to do it”, that’s a good sign. Uber was like this for me—after the first time I used it, it was clear we weren’t going to be calling cabs for that much longer, but I wasn’t sure that Uber was going to win the space.

A good question to ask yourself early in the process of thinking about an idea is “could this be huge if it worked?” There are many good ideas in the world, but few of them have the inherent advantages that can make a startup massively successful. Most businesses don’t generate a valuable accumulating advantage as they scale. Think early about why an idea might have that property. It’s obvious for Facebook or Airbnb, but it often exists in more subtle ways.

It’s also important to think about what you’re well-suited for. This is hard to do with pure introspection; ideally you can ask a mentor or some people you’ve worked with what you’re particularly good at. I’ve come to believe that founder/company fit is as important as product/market fit.

Finally, a good test for an idea is if you can articulate why most people think it’s a bad idea, but you understand what makes it good.

This is from my notes for a talk I gave at a YC event in China in 2018. Thanks to Eric Migicovsky for encouraging me to post it!

I wrote it when I thought mostly about startups; now I think mostly about AI development. I am struck by how much of it applies, particularly paragraphs 5-9.

请为科学投入更多资金 || Please Fund More Science

2025-05-25T12:21:16+00:00

# 专家提出的三种疫情终结路径

COVID-19专家认为有三种方式能让生活、健康和经济基本恢复正常：

1. **疫苗突破**：研发出能使全球R0值<1的有效疫苗  
2. **特效治疗**：开发出消除民众恐惧的疗法  
3. **防控体系**：建立完善的检测-追踪-口罩-隔离防疫文化

## 当前困境与机遇
- **政府投入不足**：联邦政府科研资金短缺，近期刺激法案中研发拨款占比极低
- **私营力量崛起**：生物科技公司和实验室正以**10倍速**推进研究，展现出惊人的创新精神

## 行动呼吁
科学家需要**资金**和**资源网络**来拯救世界。投资者和捐赠者可以：
- 将部分资金转向抗疫科研项目（包括未来流行病防御）
- 借助专业顾问快速学习评估（大量专家愿提供尽调支持）
- 公开投资意向（优质项目机会将主动涌现）

## 三重收益
1. 参与有意义事业的成就感
2. 探索全新领域的趣味性
3. 接触顶尖人才带来的乐观情绪

> 这将是改变世界的分水岭时刻——直到我们具备快速应对新病毒的能力前，世界规则已永久改变。

---------------

研究COVID-19疫情的专家们普遍认为有三种途径能让生活、健康与经济大致回归常态——

要么研发出足够有效的疫苗使全球基本传染数R0降至1以下，要么找到足够完善的治疗方案消除民众恐惧，再或是建立起成熟的检测、接触追踪、口罩佩戴与隔离的文化体系。

我衷心希望联邦政府能采取更多行动——哪怕将近期刺激法案中几个百分点的资金投入研发领域都将是巨大进步。但当前政府对科研的资助显然不足，尽管我认为私营部门与慈善机构替代政府职能的担忧不无道理，眼下确实没有更优选择。

值得欣慰的是，我从未见过任何一个领域能如此迅猛聚焦于单一难题。生物科技公司与研究实验室的响应令人惊叹，其运作效率似乎提升了十倍有余。这是创新精神的极致体现，目睹这些机构的作为令人倍感振奋。

科学家们能带领我们走出困境。他们需要的只是资金与人脉。

投资者与捐赠者们——这正是我们施以援手之处。请考虑将部分注意力与资本转向应对疫情的科研项目。（包括未来可能的大流行——我认为这将成为世界历史的分水岭，在人类具备快速抵御新病毒能力之前，一切都会不同。）

学习曲线非常陡峭，且众多专家愿意协助您开展尽职调查。参与可能改变现状的事业令人充实，尝试全新领域充满趣味，这更能重燃您的乐观精神。

若向社交圈表明您有意资助COVID-19相关研究，机会将如潮水般涌来。当最优秀的开拓者汇聚之时，永远是最佳的投资时机。

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Experts on the COVID-19 pandemic seem to think there are three ways out—that is, for life, health, and the economy to return roughly to normal.

Either we get a vaccine good enough that R0 for the world goes below 1, a good enough treatment that people no longer need to be afraid, or we develop a great culture of testing, contract tracing, masks, and isolation.

I wish that the federal government were doing much more—it would be great to see even a few percent of the recent stimulus bill go to funding R+D. But they don’t seem to be funding enough science, and although I think concerns about the private sector and philanthropy doing what the government is supposed to be doing are somewhat valid, there isn’t a great alternative right now.

On the positive side, I have never seen a field focused on one problem with such ferocity before. The response of biotech companies and research labs is amazing, and the speed they are operating at seems to have increased by more than 10x. It’s the best of the spirit of innovation, and it’s inspiring to see what these companies and research labs are doing.

Scientists can get us out of this. What they need are money and connections.

Investors and donors—this is where we can help. Please consider shifting some of your focus and capital to scientific efforts addressing the pandemic. (And future pandemics too—I think this will be a before-and-after moment in the world, and until we can defend against new viruses quickly, things are going to be different.)

The learning curve is quick, and there are a lot of experts willing to help you with diligence. It feels good to do something that might be useful, it’s interesting to do something totally new, and it will make you more optimistic.

If you make it known to your network that you want to fund efforts working on COVID-19, you’ll get flooded with opportunities. And it’s always good to invest where the best founders are congregating.

新冠疫情项目资金 || Funding for COVID-19 Projects

2025-05-25T08:01:38+00:00

# 资助抗击COVID-19初创项目的倡议

我正试图资助那些助力抗击COVID-19的初创公司/项目，因为这基本是我能提供实质性帮助的领域。当前重点资助方向包括：

## 优先领域
1. **快速生产呼吸机/防护装备**  
   - 需通过改造现有产能和创新设计实现规模化生产  
   - 属于工程难题而非科学难题（相对可行性更高）

2. **现有药物有效性筛查**  
   - 加速验证已上市药物对新冠病毒的疗效

3. **创新型疫苗研发**  
   - 重点关注与大药厂现有路径不同的技术路线

4. **非主流治疗方案**  
   - 支持大药厂 unlikely 投入的疗法研究

## 协作需求
- 公开表格收集效果不佳 → **请直接邮件联系**  
- 急寻能开展以下实验的CRO公司：  
  `人类ACE2动物模型中SARS-CoV-2病毒攻毒试验`

## 重要提醒
**最有效的防疫措施仍是倡导居家隔离**

（注：采用Markdown格式呈现，包含分级标题、重点加粗、项目符号、技术术语保留英文原词等要素，总字符数控制在原文的40%以内）

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我正尝试资助那些助力抗击COVID-19的初创企业/项目，因为这基本上是我所知能提供帮助的方式。我认为我们很快将拥有足够的检测能力，因此现在我想开始资助更多致力于以下领域的初创公司：

快速大量生产呼吸机或口罩/防护服。这需要大量的资源重组和创新思维，但庆幸的是这是个工程问题而非科学难题。
筛选现有药物的有效性。
疫苗研发的新颖路径（即不走大型制药公司已在进行的常规路线）。
大型制药公司不太可能投入研发的新型疗法。

我们曾尝试使用这份公开表格，但效果不佳；请改为直接邮件联系我。

另外，如果有人知道能在人源化ACE2动物模型中对SARS-CoV-2进行病毒攻击实验的合同研究公司，这将对我合作的初创企业大有助益。请随时联系！

当然，我认为最有效的措施依然是让人们待在家中。

---------------

I’m trying to fund startups/projects helping with COVID-19, because it’s basically the one thing I know how to do that can help. I think we will soon have enough testing capacity, so now I’d like to start funding more startups working on:

Producing a lot of ventilators or masks/gowns very quickly. This will require a lot of repurposing and creativity but thankfully is an engineering problem not a scientific ones.
Screening existing drugs for effectiveness.
Novel approaches to vaccines (i.e., not doing what the big pharma companies are already doing).
Novel therapeutics that the big pharma companies are unlikely to work on.

We tried this public spreadsheet but it didn't work that well; please email me instead.

Also, if anyone knows of a contract research company that can run a viral challenge against SARS-CoV-2 in a humanized ACE2 animal model, that would help a startup I’m working with. Please reach out!

And of course, I think the best thing to do is still to get people to stay home.

病毒 || The Virus

2025-08-19T04:04:55+00:00

尽管我仍期待事态有转机，但与我交谈的专家们认为，我们很可能面临一场全球悲剧——新冠肺炎将导致数十万人死亡。我希望社会能将其视为对未来的警示。新冠疫情虽严重，却只是一次"热身"。我认为在我们有生之年，这不太可能是最严重的新发大流行病（无论是人为还是自然起源）。我们必须为下一次更严峻的挑战做好准备。

当前，年轻健康人群应尽可能延缓感染。虽然新冠病毒对年轻人危害看似较小，但感染人数增加——尤其是症状轻微仍外出活动的人群——会加速疫情传播。而该病毒对老年人和基础疾病患者的影响尤为严重。

我预计社会将快速适应"新常态"：部分改变会长期持续（如大幅减少商务旅行、减少握手、加强洗手）；而另一些（如全民居家办公）则可能难以维持。

经济动荡的影响仍被低估，这将提醒我们：系统比想象中更脆弱。例如近期美国国债收益率暴跌，不能仅归因于新冠疫情，更揭示了复杂系统中可能发生的连锁反应。

---------------

尽管我仍希望事态能有转机，但与我交谈的专家们认为，我们很可能将面临一场全球性悲剧——数十万人因新冠肺炎丧生。

我希望社会能将其视为对未来的警示。新冠病毒固然可怕，却只是一场预演。我认为这不太可能是我们此生遭遇的最严重新型疫情（无论是否人为造成）。我们必须为下一次可能更严峻的挑战做好充分准备。

在此期间，健康的年轻人应尽可能避免感染。虽然新冠病毒对年轻人危害看似不大，但感染者增多——尤其是症状轻微仍外出活动的人群——会加速疫情传播，而该病毒对老年人和基础疾病患者的影响尤为严重。

我预计社会将迅速适应新常态。某些改变——如大幅减少商务差旅、减少握手、加强洗手——可能会长期保持；而另一些——如全民居家办公——则可能难以持续。

经济动荡的影响仍被低估，这将提醒我们：社会系统的脆弱性远超想象。例如，近期美国国债收益率暴跌不能仅归因于疫情，更是复杂系统中连锁效应的现实写照。

---------------

Although I still hope things will go differently, the experts I’ve spoken to think we are likely to face a global tragedy—hundreds of thousands of deaths from Covid-19.

I hope that society views this as a warning for the future. Covid-19 is bad, but only a warm-up. I think it’s unlikely that this is the worst new pandemic (human-created or otherwise) we’ll see in our lifetimes. We need to be ready to deal with it much better next time.

In the meantime, young healthy people should try to avoid getting Covid-19 for as long as possible. It’s true that it doesn’t seem to be very bad for young people, but more people getting it—particularly people who don’t get sick enough to stay home—will accelerate the spread, and this virus seems quite bad for old people and people with pre-existing conditions.

I expect society will shift to a new normal pretty fast. Some of these elements—e.g., much less business travel, much less handshaking, much more handwashing—I expect to just stick. Some others—e.g., people working from home all of the time—I expect to not stick.

The economic disruption is still probably under-appreciated and will remind us that our systems are more fragile than we think. For example, I do not think the recent plunge in US Treasury yields is explained by Covid-19 alone, but rather a reminder of cascading effects that can happen in a complex system.