译：Sam Altman：温和的奇点

原文： https://blog.samaltman.com/the-gentle-singularity
作者： Sam Altman
译者： Gemini 2.5 Pro

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。人类即将建成数字超级智能，而至少到目前为止，这一切远没有想象中那么诡异。

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.

机器人还没有在街上行走，我们大多数人也还没整天和 AI 对话。人们依旧会死于疾病，我们仍然无法轻易进入太空，宇宙中还有很多我们不理解的事情。

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.

然而，我们已经构建了在许多方面比人类更智能的系统，并能显著放大使用者的产出。最艰难的工作已经完成；那些让我们得以创造出 GPT-4 和 o3 这样系统的科学洞见来之不易，但它们将带我们走得更远。

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 将在很多方面为世界做出贡献，但由 AI 驱动的更快科学进步和更高生产力所带来的生活质量提升将是巨大的；未来可以比现在好得多。科学进步是整体进步的最大驱动力；想一想我们可能拥有的更多东西，就非常激动人心。

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.

在某种重要意义上，ChatGPT 已经比历史上任何人类都更强大。每天有数亿人依赖它处理日益重要的任务；一个微小的新功能可以产生巨大的积极影响；一个微小的未对齐，乘以数亿人，也可能造成巨大的负面影响。

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，能够完成实际认知工作的智能体已经出现；编写计算机代码的方式将永远改变。2026年，能够发现新洞见的系统可能会出现。2027年，或许会看到能够在现实世界中执行任务的机器人。

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.

更多的人将能够创造软件和艺术。但世界对这两者的需求也会大得多，只要专家们拥抱新工具，他们可能仍然会比新手强很多。总的来说，一个人在2030年能完成的工作量远超2020年，这将是一个显著的变化，许多人会想办法从中受益。

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年代可能不会有天翻地覆的不同。人们仍然会爱他们的家人，表达他们的创造力，玩游戏，在湖里游泳。

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.

但在其他仍然非常重要的方面，2030年代可能会与以往任何时代都截然不同。我们不知道我们能超越人类水平的智能多远，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.

在 2030 年代，智能和能源——也就是想法，以及将想法变为现实的能力——将会变得空前充裕。这两者长期以来一直是限制人类进步的根本因素；有了充裕的智能和能源（以及良好的治理），理论上我们可以拥有其他任何东西。

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.

我们已经生活在令人难以置信的数字智能之中，在最初的震惊之后，我们大多数人已经习以为常。我们很快就从惊叹 AI 能生成一段优美的文字，转为好奇它何时能写出一部优美的小说；从惊叹它能做出拯救生命的医疗诊断，转为好奇它何时能开发出治愈方法；从惊叹它能编写一个小小的计算机程序，转为好奇它何时能创建一个全新的公司。奇点就是这样发生的：奇迹变为常态，然后成为基本要求。

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.

我们已经从科学家那里听说，他们的生产力比使用 AI 之前提高了一两倍。先进的 AI 之所以引人注目，原因有很多，但也许没有什么比我们能用它来加速 AI 研究本身更重要了。我们或许能发现新的计算基底、更好的算法，以及谁也说不准的其他东西。如果我们能用一年甚至一个月的时间完成过去十年才能完成的研究，那么进步的速度显然会截然不同。

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得更深的科学洞见，并协助我们创造更好的 AI 系统。当然，这和一个 AI 系统完全自主地更新自己的代码不是一回事，但这无疑是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.

还有其他的自我强化循环正在发挥作用。经济价值的创造已经启动了一个飞轮效应，推动着基础设施的复合式增长，以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（在某种意义上，是能够建造其他数据中心的数据中心）也并不遥远。

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.

如果我们必须用老办法制造出第一批一百万个类人机器人，但之后它们可以运营整个供应链——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，这些机器人又能建造更多的芯片制造厂、数据中心等等，那么进步的速度显然会截然不同。

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.)

随着数据中心生产的自动化，智能的成本最终应该会趋近于电力的成本。（人们常常好奇一次 ChatGPT 查询消耗多少能源；平均一次查询大约消耗 0.34 瓦时，相当于一个烤箱一秒多一点的用电量，或一个高效灯泡几分钟的用电量。它还消耗约 0.000085 加仑的水，大约是十五分之一茶匙。）

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，而人类适应几乎任何事物的能力也将一如既往。会有非常困难的部分，比如整类工作的消失，但另一方面，世界将变得如此富有、如此迅速，以至于我们能够认真考虑以前从未敢想的新政策理念。我们可能不会一蹴而就地采纳新的社会契约，但几十年后回首，这些渐进的变化将汇聚成巨大的变革。

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.

如果历史可为借鉴，我们会找到新的事情去做，产生新的欲望，并迅速吸收新工具（工业革命后的职业变迁就是一个很好的近代例子）。期望会提高，但能力也会同样迅速地提高，我们都会得到更好的东西。我们将为彼此创造出越来越美好的事物。人类相对于 AI 有一个长期、重要且奇特的优势：我们的天性决定了我们关心他人，关心他们的想法和行为，而我们对机器则不太在乎。

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.

一千年前自给自足的农民看到我们许多人今天的工作，会说我们做的是“虚假的工作”，会认为我们不过是在玩游戏自娱自乐，因为我们有充足的食物和难以想象的奢侈品。我希望一千年后的人们回顾我们今天的工作时，也会觉得它们非常“虚假”，但我毫不怀疑，对于从事这些工作的人来说，它们感觉极其重要和令人满足。

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”.

新奇迹实现的速度将是巨大的。今天我们甚至很难想象到2035年我们会发现什么；也许我们会在一年内解决高能物理问题，然后在下一年开始太空殖民；或者在一年内取得重大的材料科学突破，然后在下一年实现真正的高带宽脑机接口。许多人会选择以大致相同的方式生活，但至少有些人可能会决定“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，这听起来很难理解。但亲身经历它可能会感觉印象深刻但尚可应对。从相对论的视角看，奇点是逐步发生的，融合是缓慢进行的。我们正在攀登技术指数级进步的漫长弧线；向前看它总是显得陡峭，向后看则显得平坦，但它是一条平滑的曲线。（回想一下2020年，如果当时有人说2025年就会有接近 AGI 的东西，听起来会是什么样，再对比一下过去5年的实际情况。）

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:

伴随着巨大的好处，我们也面临着严峻的挑战。我们确实需要解决技术上和社會上的安全问题，但鉴于其经济影响，广泛地普及超级智能的访问权限也至关重要。最佳的前进道路可能是这样的：

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).
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.
3. 解决对齐问题，这意味着我们能有力地保证 AI 系统学习并践行我们作为一个集体真正想要的长期目标（社交媒体的信息流就是未对齐 AI 的一个例子；驱动它们的算法非常擅长让你不停地刷下去，并且清楚地了解你的短期偏好，但它们是通过利用你大脑中覆盖了长期偏好的某种机制来实现这一点的）。
4. 然后，专注于让超级智能变得廉价、普及，并且不过度集中于任何个人、公司或国家。社会是坚韧、富有创造力且适应迅速的。如果我们能驾驭人们的集体意愿和智慧，那么尽管我们会犯很多错误，有些事情会出错得非常严重，但我们将快速学习和适应，并能够利用这项技术获得最大的好处和最小的坏处。在社会必须决定的广泛边界内，给予用户大量的自由，似乎非常重要。世界越早开始讨论这些广泛的边界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，不仅仅是 OpenAI）正在为世界构建一个大脑。它将是高度个性化的，每个人都能轻松使用；我们将受限于好的想法。很长一段时间里，创业圈的技术人员都嘲笑那些“点子大王”；那些只有一个想法，然后到处找团队来实现它的人。现在在我看来，他们即将迎来自己的高光时刻。

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 现在有很多身份，但首先，我们是一家超级智能研究公司。我们面前还有很多工作要做，但我们面前的大部分道路已经被照亮，黑暗的区域正在迅速退去。能够从事我们现在所做的事，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.

廉价到无法计量的智能已经触手可及。这样说可能听起来很疯狂，但如果我们在2020年告诉你我们今天会达到这个水平，那听起来可能比我们现在对2030年的预测还要疯狂。

May we scale smoothly, exponentially and uneventfully through superintelligence.

愿我们能平稳、指数级且波澜不惊地穿越超级智能的时代。