“温和的奇点”｜sam altman 最新雄文

sam altman 一年也就发2-3篇博客文章。

昨天发的这一篇“the gentle singularity”，和ilya前天的演讲一样语言朴素，不甩大词，不像某些大V显摆“高级”的“认知”，

但是，如果你和我一样把它阅读5678遍，你会发现非常有指导实践的价值。

title：the gentle singularity

author：sam altman

date：2025-06-10

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.

我们已经越过了"事件视界"，起飞已经开始。人类正接近构建 数字超级智能（super AGI），而且至少目前为止，这一切远没有想象中那么奇异。

事件视界(event horizon)：物理术语。在广义相对论里，黑洞的“事件视界”是把时空切成两部分的那条无形边界：一旦任何粒子或信息跨过这条线，就再也无法返回，不论你多强大、速度多快，连光都逃不出来。

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.

机器人还没上街，大多数人也没整天和AI说话。人类仍然会死于疾病，我们依然无法轻易进入太空。对于宇宙，我们仍然有很多不了解的地方。

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.

尽管如此，我们最近已经构建出在许多方面都比人类更聪明的系统，并且能够大幅提升人们使用它们所取得的成果。工作中原本最不可能实现的部分现已成为过去；让我们开发出GPT-4和o3这类系统的科学洞见来之不易，但这些洞见将指引我们走向远方。

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.

AI将在诸多方面为世界作出贡献，但借助AI加速科学进步和提高生产力所带来的生活质量提升将是巨大的；未来可能远远胜过现在。科学进步是整体进步最强大的推动力；一想到我们还能获得多少新的成果，便令人无比振奋。

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.

从某种宏观意义上说，ChatGPT已经比有史以来任何一个人都更强大。每天有数以亿计的人依赖它，并将其用于越来越重要的任务；一个微小的新功能就能带来巨大的积极影响；而一个小小的 错误对齐（misalignment）乘以数亿人则可能造成巨大的负面影响。

错误对齐（misalignment）:术语。举例：算法短视频的本质，就是一个错误对齐的ai。这个ai的目的与你作为用户的价值是不对齐的。

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.

2025年已经出现了 能够执行真正认知工作的智能体；编写计算机代码的方式将从此不再相同。2026年很可能会出现 能够发现全新见解的系统。2027年或许会出现 能够在现实世界中执行任务的机器人。

简单来说：2025年，L3 AGI；2026年，L4 AGI；

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.

将有更多的人能够创作软件和艺术作品。但世界需要更多的软件和艺术，并且 只要专家们拥抱这些新工具，他们很可能仍然比新手强得多。总体而言，一个人在2030年所能完成的工作将远远超过其在2020年所完成的，这是一个引人注目的变化，许多人将想办法从中受益。

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.

在最重要的方面，2030年代可能并不会与现在有天差地别。人们仍然会爱着家人，发挥创造力，玩游戏，在湖中游泳。

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.

但 在一些依然非常重要的方面，2030年代很可能会与以往任何时代截然不同。我们不知道在超越人类智能水平这件事上能走多远，但我们即将找到答案。

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.

到了2030年代，智能和能源——也就是创意，以及将创意付诸实践的能力——将会变得异常富足。这两者长期以来一直是限制人类进步的根本因素；而有了充裕的智能和能源（再加上良好的治理），理论上我们可以获得其他一切。

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 life-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.

实际上，我们已经在与令人惊叹的 数字智能共存。在经历了最初的震撼之后，我们大多数人已经相当习惯于它了。我们很快从惊叹AI能够生成一段优美的文字，转而想知道它何时能创作出一部优美的小说；或者从惊叹它能够作出拯救生命的医疗诊断，转而想知道它何时能研发出治愈疾病的方法；再或者从惊叹它能够创建一个小型计算机程序，转而想知道它何时能创造出一家全新的公司。这就是奇点发展的过程：奇迹变成日常，随后成为"上桌的前提"。

table stakes: 扑克术语，指每位玩家在牌局开始前必须先放在桌面上的赌注。延伸为基本资格，入门的最低门槛。

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.

据科学家反映，他们的生产力相较于有AI之前提高了两到三倍。高级AI之所以引人注目有很多原因，但也许最重要的是我们可以利用它更快速地开展AI研究。我们可能会发现新的计算载体、更好的算法，以及其他谁也无法预料的事物。如果我们能在一年甚至一个月内完成过去十年的研究，那么进步的速度显然将大不相同。

高级AI(advanced AI)：同ilya昨天说的“the best AIs”（最好的ai）。ai和人一样是智能体，不可一概而论，而是要以智能水平进行区分。

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.

从现在开始，我们已经构建的工具将帮助我们获取更多科学洞见，并协助我们创造更好的AI系统。当然，这与AI系统完全自主更新自身代码并不是一回事，但不管怎样，这可以看作 递归式自我改进的雏形。

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.

还有其他 自我强化的闭环在发挥作用。经济价值的创造已经启动了基础设施扩建的飞轮，用于运行这些日益强大的AI系统。而能够制造其他机器人的机器人（以及从某种意义上说，能够建造其他数据中心的数据中心）离实现也已经不远了。

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.

如果我们不得不用传统方式制造首批一百万个人形机器人，但之后它们可以操作整个供应链——挖掘和提炼矿物、驾驶卡车、运营工厂等等——来制造更多的机器人，而这些机器人又能够建造更多芯片制造设施、数据中心等，那么进步的速度显然将大不相同。

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.)

随着数据中心生产实现自动化，智能的成本最终应该会趋近于电力成本。（人们经常好奇一次ChatGPT查询消耗多少能量；平均每次查询约消耗0.34瓦时，大致相当于烤箱运行一秒多钟或一个高效灯泡亮几分钟所用的能量。每次查询还约耗水0.000085加仑，约等于十五分之一茶匙的水量。）

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.

技术进步的速度将持续加快，而且人类仍然能够适应几乎任何事物。确实会有非常棘手的方面，比如整个职业类别的消失；但另一方面，世界将变得极其富裕且富裕得非常之快，我们将能够认真考虑一些以往根本不可能的新政策理念。我们或许不会一下子就订立全新的社会契约，但当我们在几十年后回顾时，这些渐进的变化将累积成巨大的转变。

基于我的理解，唯一的出路是UBI，集体发钱。让所有人来快速学习，终身学习，适应并驾驭ai，似乎很难。不愿意或不指望ubi的人，就得每天用ai，学习ai知识和工具。

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.

如果历史可以作为借鉴，我们将会想出新的事情去做，也会产生新的渴望，并能迅速地吸纳新工具（工业革命后的就业变迁就是一个很好的近期例子）。人们的期望会提高，但能力也会同样快速地提升，我们都将获得更好的东西。我们将为彼此创造出越来越多奇妙的事物。相较于AI，人类在长期方面拥有一个重要而有趣的优势：我们的天性使我们在乎他人及其所思所为，而我们并不怎么关心机器。

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.

一千年前的一位自给自足的农民，如果看到我们许多人如今所做的事情，可能会说我们的工作都是“假工作”，并觉得我们只是因为有充足的食物和难以想象的奢侈品而在玩游戏自娱自乐。我希望当我们看到一千年后的那些工作时，也会觉得那些工作非常“虚假”；而我毫不怀疑，对于从事那些工作的人来说，那些工作将让他们感到无比重要且令人满足。

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”.

新奇迹诞生的速度将极为惊人。今天我们甚至难以想象到了2035年我们会发现些什么；也许我们会在某一年解决高能物理难题，而下一年就开始太空殖民；或者某一年在材料科学上取得重大突破，而下一年就实现真正的高带宽脑机接口。许多人将选择几乎以同样的方式过他们的生活，但至少会有一部分人可能决定“接入”。

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.)

展望未来，这一切听起来让人难以完全理解。但真正经历时，可能会让人觉得惊叹却依然可控。从相对的角度来看，奇点是逐步到来的，融合也是缓慢发生的。我们正沿着技术指数级进步的长弧攀登；向前看，这条弧线总显得陡直，向后看，它又仿佛平坦，但实际上它是一条平滑的曲线。（回想2020年，如果有人说到2025年我们将接近拥有AGI，那听起来会有多夸张；再看看过去5年的实际经历。）

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:

巨大的潜在收益伴随着严峻的挑战。我们确实需要在技术和社会层面解决安全问题，但随后，鉴于超级智能所带来的经济影响，将其访问权限广泛开放就变得至关重要。最好的前进路径可能是：

1. 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).

   解决对齐问题，也就是说我们能够可靠地确保AI系统在长期内学习并朝着我们集体真正想要的方向行事。（社交媒体的信息流就是 “错误对齐AI”的一个例子；驱动这些信息流的算法极擅长诱使你不停地滚动浏览，并且清楚地了解你的短期偏好，但它们正是通过利用你大脑中的某些机制让你的短期偏好凌驾于长期偏好之上来实现这一点的。）

2. 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.

   接下来要专注于让超级智能变得廉价、广泛可及，并且不至于过度集中于任何个人、公司或国家。社会具有韧性和创造力，并且适应能力很强。如果我们能够凝聚全人类的意志和智慧，那么尽管我们会犯很多错误，也会出现一些严重的问题，我们仍能快速地学习和适应，并利用这项技术使好处最大化、坏处最小化。在由社会决定的宽泛界限内给予用户充分的自由，这一点显得非常重要。全球越早开始讨论这些界限是什么，以及我们如何定义“集体对齐”，就越好。

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.

我们（整个行业，而不仅仅是 OpenAI）正在为这个世界打造一个“大脑”。这个“大脑”将是 高度个性化且每个人都能轻松使用的；我们未来 唯一的限制将是有没有好的创意。长期以来，创业圈的技术人员一直嘲笑那些“点子家伙”——那些只有一个主意就想找团队来实现的人。而现在在我看来，属于他们大显身手的日子即将到来。

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.

OpenAI 目前涉足许多方面，但首先，我们是一家超级智能研究公司。我们面前还有大量工作要完成，但此刻我们前方的大部分道路都已经被照亮，黑暗未知的区域正在快速缩小。能够从事我们所做的事情，我们感到无比感激。

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.

“智能廉价到无需计价”已在掌握之中。这样说或许听起来很疯狂，但如果我们在2020年就告诉你我们今天会达到现在的局面，当时听起来可能会比我们目前对2030年的预测还要疯狂。

这篇文章发布的同时，openai把o3价格降低到比gpt-4o还便宜。顶级ai，底部价格。

May we scale smoothly, exponentially and uneventfully through superintelligence.

愿我们能够 以平稳、指数级且波澜不惊的方式扩展到超级智能阶段。

翻译一下：祝愿世界和平。奇点在快速到来，希望是一个“温和的奇点”。

杂想

与文章有关的几个想法。

首先，我来泄露一个“天机”：英文翻译，最好的工具就是openai deep research。一次成型，表达准确，语言恰当，基本没有修改余地（以本文为例，我只加了几条注释）。关键是不会漏，不会乱改，不会错。

我很多年前是靠翻译吃饭的（乔布斯去世时的bloomberg business week专刊，2/3是我翻译的）。收费很贵，一个国庆假期能挣50k。但是，我现在用deep research的翻译工作流，只能感叹幸亏转行了🤣

这个经验是很有价值的。基本上，人工翻译和低质量ai翻译，不应该存在了。你就把deep research当成一个翻译agent即可。可以直接出活的那种。

其次，用一个具体的例子来证明一个可怕的观点。

观点：ai时代，智能已经不具有“独特性”（以前，高级智能是“智人”独占），而是沦为一种资源，和电力一样的资源，没什么特别的，而且超级便宜。

有多便宜？智能成本最终会逼近电价，便宜到几乎不需要计价。sam altman 给出了当前平均一次 chatgpt对话的能量消耗：0.34 Wh 电 + 0.000085 gal 水。

例子：以sam altman的最新blog为例，15年前，翻译这样的一篇文章，价格大约在1000元左右（千字400）。现在，deep research一下，10分钟搞定。更快，更好，价格几乎为0。（那么，为什么大学里还有英语专业？🤣）

不少类型的智力劳动，跟翻译劳动没有本质区别。

证明完毕。