# Something Big IS Happening. It's Bigger than you Think.
**作者**: Dan McAteer
**日期**: 2026-05-14T18:38:58.000Z
**来源**: [https://x.com/daniel_mac8/status/2054994899422826592](https://x.com/daniel_mac8/status/2054994899422826592)
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Think back to February 2026.
@mattshumer_'s viral essay “Something Big is Happening” articulated a feeling: AI had started to work. AI was really happening. And like the early days of Covid, we didn’t appreciate what that meant.
A few months later, we have more than a feeling.
We have exponential progress staring us in the face.
Three months ago, the evidence was anecdotal: software engineers took a holiday break, and a few famous ones noticed that AI coding agents worked. They told their audiences about it and the swell started to grow. Bright minds like @deanwball began saying that Claude Code + Opus 4.5 was AGI.
Since then, the evidence has become institutional: AISI, METR, Anthropic-adjacent coalitions, cyber-security groups, huge AI-science financing.
If Matt’s essay was “we can see UFOs in the sky”, this essay is:
> “The aliens have landed. They want to speak to our leader.”
# Agents Become Undeniable
There are two recent results that show agents have become largely autonomous.
1. Claude Mythos Solves AISI’s Full Cyber Attack Simulations
Claude Mythos preview is the first model to solve both of AI Security Institute’s Cyber attack simulations. “The Last Ones” is one of the cyber tests Mythos successfully completed in 6/10 attempts. It is estimated to take a human attacker 20 hours to complete. The other cyber attack test Mythos completed, named “Cooling Tower”, was previously unsolved. Mythos succeeded in 3/10 attempts.
In November 2025 the approximate doubling time for AISI cyber test task duration was 8 months. By February of 2026 it had accelerated to ~4.7 months. Both Mythos and GPT-5.5 are ahead of the 4.7 month trend.
If that trend holds, AI task duration, at least for cyber tasks, will be super-exponential. The tasks models can complete are getting longer, and the rate of improvement is itself speeding up.
There’s no reason to assume this jump in capability remains limited to cyber tasks. Anthropic has confirmed Mythos is a “new general-purpose language model” and say the cyber capabilities “emerged as a downstream consequence of general improvements in code, reasoning, and autonomy.”
Anthropic decided to make Mythos available to a select group of organizations for cyber-defense purposes first, because that’s most important for society. This level of capability should generalize to other domains too. There is no reason to believe it won’t.
2. Mythos Breaks METR
METR measures something simple: how long a task takes a skilled human, and whether an AI agent can complete tasks of that duration with a given success rate. Their “50% time horizon” is the point where an agent succeeds half the time on tasks that would take a human expert that long.
Claude Mythos Preview is now past the edge of that measurement. METR’s latest update added early Mythos results and warned that measurements above 16 hours are unreliable with the current task suite.
The reason this is interesting is not just “Mythos can do longer tasks.” It’s that our AI models are now outpacing our ability to benchmark them. We are starting to run out of clean, well-measured tasks long enough to test what these agents can do.
AI went from “can do tasks that would take human a couple minutes” to “tasks that would take a human a couple days” in a couple years. At least. METR says the measurements are unreliable.
Cyber is the sharpest real-world test case, but Anthropic says the capability came from general improvements in code, reasoning and autonomy. The METR result backs that up.
Agents can now perform real, economically valuable work. They are getting better fast.
> Agents have become undeniable.
That’s great if the good guys have them. Not so great if the bad guys do.
# The Security World Responds
This is exactly why Anthropic did not just ship Mythos to everyone.
Instead, they launched Project Glasswing: a coordinated effort with AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, Palo Alto Networks and others to use Mythos for cyber defense first.
Think about that for just a minute.
One of the leading AI labs built a general-purpose model so capable at finding and exploiting software vulnerabilities that they decided the responsible first release was not a chatbot product but a security coalition. Some criticize the move as pure “marketing hype.” But it would have been a better marketing strategy to release Mythos widely and keep growing revenue.
Anthropic says Mythos has already found thousands of high-severity vulnerabilities, including bugs in every major operating system and every major web browser. Some were quite old. One was a 27-year-old vulnerability in OpenBSD. Another was a 16-year-old bug in FFmpeg that automated testing tools had hit millions of times without catching.
And again: Mythos was not trained specifically to be a cyber weapon. The cyber capabilities emerged from improvements in coding, reasoning and autonomy.
That’s the part that should send a chill down everyone’s spine.
If you have an AI agent that can autonomously find subtle bugs, chain vulnerabilities together, reverse engineer binaries, and produce working exploits, you have something that can change the balance between attackers and defenders.
In the short term, this is dangerous. Attackers only need to find one path in. Defenders have to protect everything. If models like Mythos become widely available before defenders are ready, the bad guys get leverage too.
But this is also why the upside is so large.
For the first time, the good guys may have a tool that can search through huge amounts of critical software and find the hidden cracks before attackers do. Anthropic is putting up $100 million in usage credits for Project Glasswing and additional participants, plus millions more for open-source security organizations.
That’s why it’s so important that powerful AI is pointed in the right direction.
It could become a true boon to society, but only if we use it that way.
Powerful AI could help harden the software that banks, hospitals, governments, browsers, phones, servers, and open-source projects depend on.
I expect this pattern to repeat. The same capability that makes AI risky also makes it useful. The same autonomy that can be abused can also be used to defend, discover, repair and build.
> It’s not so much a double-edged sword as it is a double-edged cognitive nuclear weapon.
# The Next Frontier Is Doing AI Research
And cyber is not the end of the story.
It’s just the first place where the rubber hit the road for civilizational infrastructure.
The next frontier is AI systems doing AI research itself. Then, potentially, autonomous scientific research writ large.
@jackclarkSF, co-founder of Anthropic and author of Import AI, recently said he now thinks there is a 60%+ chance that we get no-human-involved AI R&D by the end of 2028. In plain English: an AI system powerful enough that it could autonomously build its own successor.
It sounds sci-fi, until you look at what AI research is and what models are already good at.
Think about it. What is AI research? Some of it is inspiration. New ideas. Taste. Weird intuition. The thing that causes someone to see a problem from an original perspective.
But much of it is something else.
A lot of AI research is writing code, running experiments, cleaning data, reading papers, reproducing results, debugging training runs, optimizing kernels, comparing model outputs, searching through failed attempts, and trying again.
That is exactly the kind of work agents are getting good at.
@karpathy's autoresearch project proved that today’s AI models can already do autonomous research to improve a model’s own hyperparameters. What happens when you can not only improve a model’s hyperparameters, but when you can autonomously improve the algorithms, the architecture, the data mix, all of the ingredients that go into making a model stronger than it currently is?
This is where the latest METR results come into the picture. Right now models can do coding tasks that might take a human SWE a couple days. Soon, models may be capable of research tasks that would take a human researcher even longer.
AI is not a replacement for the best human researchers yet. Not some magic oracle that invents the Transformers successor on command.
But a synthetic colleague that can take more and more of the toil.
And once AI can automate large chunks of AI research, progress stops being limited only by the number of human researchers who can think, code, test and iterate. Progress starts being limited by compute, data, evaluation, safety, and how well we can direct these systems.
Then the human researchers can do what they do best: sit around being inspired. And the autonomous AI researchers can do what machines do best: carry out the toil without ever getting bored or tired.
Because if AI helps build better AI, and better AI helps build even better AI, the feedback loop becomes recursive. The thing improving is also becoming part of the improvement process.
That is why “Something big is happening” turns into “It’s Bigger than you Think”. Humans have a very hard time conceptualizing exponential progress. It’s not how we evolved.
AI progress may literally accelerate past the point that we can imagine due to our inherent cognitive limitations.
And if AI can automate its own research, it is plausible that it will eventually automate the whole of scientific research. It may become capable of autonomously creating explanatory scientific knowledge. And it could do this 24/7/365. This is harder until AI can run experiments in atomic space, but it is plausible.
The quantum physicist and scientific philosopher David Deutsch defines true wealth as “the set of physical transformations you have the knowledge to effect on the world.”
Powerful AI may come to the point where it can autonomously create that type of wealth, nonstop, for as long as we keep the datacenters running.
# The Upside Is Not Just Productivity
This is why the upside is so much bigger than “AI makes workers more productive.”
That framing is true, but it is too small. It makes AI sound like better email, faster spreadsheets, cheaper customer support and faster software engineers. Useful. Valuable. Economically important.
But not world-historical.
The real upside is not that AI lets us do the same things faster. It is that AI may let us do things we cannot do at all today.
The upside is that AI unlocks new vistas for the human race.
It may let us search spaces that are too large for human teams. Read literatures too vast for any one researcher. Run simulations, propose experiments, debug failures, generate hypotheses, write code, compare results, and keep going after every human in the lab has gone home.
That is larger than productivity.
That is acceleration of discovery.
One concrete example is drug discovery. Isomorphic Labs recently raised $2.1 billion to build AI systems for designing new medicines. That does not mean AI has already cured disease. It does not mean biology is solved. Biology is messy, expensive, physical, and brutally humbling.
But it does mean some of the smartest capital in the world is now betting that AI can change the economics of discovering medicines.
Isomorphic’s stated mission is to “solve all disease.”
That’d be a pretty big deal.
Because the optimistic case for AI is not “your boss gets a cheaper intern.” The optimistic case is that humanity gets more shots on goal against cancer, Alzheimer’s, rare diseases, antibiotic resistance, aging, climate, materials science, energy, and every other hard problem where progress is bottlenecked by the rate at which we can create and test knowledge.
Many of us are still thinking about AI as a labor technology. A productivity tool.
We ask: whose job does it replace? Whose salary does it compress? Which company gets more efficient?
Those are important questions. We should take them seriously. There will be disruption. There will be losers. There will be people caught in the transition who did not ask to be part of a civilizational technology experiment. That may be most of us. We should not hand-wave that away.
But labor is not the deepest category here.
Knowledge is.
If AI becomes a system for creating new knowledge, then the relevant comparison is not outsourcing or automation. It is the printing press. The scientific method. The computer. The internet. Maybe something larger than all of them, because this time the tool is not just storing or transmitting human thought. It is beginning to participate in the process of generating it.
It would be a force capable of creating new knowledge. Humans have been the only known example of that force for all of history.
That is what makes this moment:
> Bigger than you Think.
The same systems that can find software vulnerabilities may help secure hospitals and banks. The same systems that can automate parts of AI research may help automate parts of biology, chemistry, materials science, engineering and medicine. The same systems that can displace some forms of work may also give millions of people access to tutors, coaches, collaborators, translators, programmers, analysts and teachers that would previously have been unavailable to them at any price.
Imagine a world where a kid in a small town can learn math, coding, physics, writing, biology, design and entrepreneurship with an infinitely patient tutor that adapts to them personally.
Imagine a nurse retraining into software. A factory worker learning robotics. A founder building a product that would have required a twenty-person engineering team five years ago. A patient with a rare disease getting an AI-assisted explanation of the latest research that their local doctor would never have had time to read.
None of this is guaranteed.
That part is important.
Powerful AI does not automatically distribute itself fairly. It does not automatically cure disease. It does not automatically make people wiser, institutions better, or society more humane. It can concentrate power. It can accelerate conflict. It can widen inequality. It can flood the world with synthetic persuasion and automated offense.
But it is also not realistic to look at a technology that may automate large parts of science and only see danger.
The dangers are real. The upside is also real.
And the upside is not marginal.
If we can point these systems in the right direction, they may become a way for civilization to solve problems faster than the problems compound. They may help us harden the digital world, discover new medicines, invent new materials, teach new skills, build new institutions, and create forms of wealth that are not just financial, but physical, scientific, educational and human.
> They may help us defer death. They may help us expand life.
That is the version of “something big is happening” that I think people still underestimate.
Greater than smarter models.
Greater than better coding agents.
Greater than productivity software with a chat box.
A new engine for creating knowledge.
That is why we must get this right.
# The Dangers Are Real. They Are Not The Whole Story.
This is where the public conversation usually breaks down. For that reason, this is where we need to come together.
One side sees the upside and treats the risks like annoying footnotes. The other side sees the risks and treats the upside like a marketing hallucination.
Both are wrong.
The closer you get to approaching truth, the more you perceive paradox.
F. Scott Fitzgerald said:
> “The test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.”
The risks are not imaginary. We just walked through one of them. A model that can autonomously complete cyber attack simulations is not a toy. A model that can find hidden vulnerabilities, chain them together, and produce working exploits can make the world less secure if it is released carelessly or used by the wrong people.
That is not doomposting.
That is just what the capability is.
The same is true in other domains. If AI can accelerate research, it can accelerate good research and bad research. If AI can persuade, it can teach and manipulate. If AI can code, it can build useful software and malware. If AI can discover new biology, it can help cure disease and lower the barrier to biological misuse.
This is the basic shape of powerful technology.
Fire cooks food and burns cities. Electricity lights hospitals and powers electric chairs. Nuclear physics gave us both abundant energy and weapons that can end civilization. The internet connected the world and also gave us misinformation at planetary scale.
AI is not different because it has no downside.
> AI is different because the downside and upside both scale with intelligence.
That is why I do not think the right posture is acceleration at all costs. It is also why I do not think the right posture is fear at all costs.
The right posture is sincere courage.
We need strong evaluations. We need responsible release decisions. We need security coalitions like Project Glasswing. We need better monitoring for dangerous capabilities. We need model labs to cooperate where they should cooperate, compete where they should compete, and be honest about what their systems can do.
We need governments that understand the technology well enough to regulate it without strangling the parts of it that could help humanity most.
We need builders who do not treat every safety concern as cope from people who do not understand progress.
And we need safety people who do not treat every expression of hope as naivete.
We need balance.
Because the scary truth is that we probably need the powerful systems to help manage the risks created by the very same powerful systems.
That is already what Project Glasswing points toward. The answer to AI-enabled cyber risk is not simply “never build capable models.” The answer is to make sure defenders get the best tools first, to harden the systems society depends on, and to build institutions that can react at the speed this technology is moving.
The same pattern may apply everywhere.
We may need AI to help audit AI. AI to help secure AI. AI to help explain AI. AI to help discover the medicines, materials, energy systems and defensive tools that make the next world more survivable than this one.
There’s no way around it. We can’t turn back the clock.
That does not mean we should trust the machines blindly.
It means we should stop pretending the only choices are worship and panic.
The question is not whether AI is good or bad in the abstract.
The question is whether we can build, deploy and govern it in a way that lets the upside outrun the danger.
That is the real stakes of this moment.
Not whether AI is impressive. It is.
Not whether AI is dangerous. It is.
Whether we can become earnest enough, fast enough, to use something this powerful for human flourishing instead of letting it become another force that overwhelms us.
# Can Humanity Grow Up Fast Enough?
That, to me, is the pertinent question.
AI progress is real.
The models will get much more capable.
The stakes are civilizational.
The question is whether our institutions, norms, laws, labs, companies, and personal habits can mature quickly enough to meet what we are building.
Because the technology won’t wait for us to become wise.
The models are improving. The agents are getting longer-horizon. The benchmarks are straining. The security world is reorganizing. AI research itself is starting to look automatable. AI-for-science is attracting billions of dollars.
This is already happening.
The choice is not whether to live in the age of AI.
The choice is what kind of age of AI we are going to build.
We can build one where power concentrates, institutions lag, attackers get leverage, workers are treated as disposable, and everyone slowly loses trust in what they see, read and hear.
Or we can build one where powerful AI is pointed at the bottlenecks that actually make human life worse: disease, insecurity, ignorance, fragility, wasted talent, broken institutions, brittle software, slow science, and the fact that most people never get access to the kind of personalized education, mentorship and tools that would let them become what they could become.
That second path is not automatic.
No path is.
It has to be chosen.
It has to be built.
It has to be defended.
> Humanity has to walk the walk. And we can.
And it has to be done by people who can hold two thoughts in their head at once: this technology is dangerous, and this technology is one of the greatest opportunities humanity has ever had.
The public conversation keeps trying to collapse AI into a single moral category: savior or monster, bubble or apocalypse, productivity tool or existential threat.
But the reality is deeper than that.
AI is becoming a new layer of civilization. A layer that can create civilization level software, search for vulnerabilities, teach people, assist researchers, compress expertise, automate experiments, discover patterns, generate plans, and maybe eventually create new scientific knowledge.
That is why this feels so difficult to reason about. We are not just adding a new app to the economy. We are adding something closer to a new kind of cognitive infrastructure.
It is unprecedented.
A civilization with more intelligence available to it is not automatically a better civilization.
But it is a civilization with greater possibility.
More ability to see hidden cracks before they break. More ability to test ideas before they become dogma. More ability to discover treatments before people die waiting. More ability to teach people before their potential closes. More ability to build tools that used to require institutions. More ability to turn knowledge into real transformations in the world.
That is what I think Matt was pointing at in February.
The feeling that something had changed.
The sense that the old categories were no longer enough.
The awareness that we were watching the beginning of something whose full shape had not arrived yet.
A few months later, the shape is clearer.
Agents have become undeniable.
The security world is responding.
AI research itself may be next.
AI-for-science is becoming a serious institutional bet.
And the upside, if we aim this correctly, is not just faster work.
It is faster discovery.
Faster learning.
Faster healing.
Faster building.
Maybe, eventually, a faster path toward a world with less needless suffering and more human possibility.
A world where we all smile a lot more.
Something big is happening.
But it is bigger than another model release.
Bigger than another benchmark.
Bigger than another viral AI moment.
> The thing that is happening is that intelligence is becoming civilizational infrastructure.
And if we do this right, that infrastructure may help humanity heal, learn, build, discover and adapt at a scale we still do not know how to imagine.
## 相关链接
- [Matt Shumer reposted](https://x.com/mattshumer_)
- [Dan McAteer](https://x.com/daniel_mac8)
- [@daniel_mac8](https://x.com/daniel_mac8)
- [10K](https://x.com/daniel_mac8/status/2054994899422826592/analytics)
- [@mattshumer_](https://x.com/@mattshumer_)
- [“Something Big is Happening”](https://x.com/mattshumer_/status/2021256989876109403?s=20)
- [@deanwball](https://x.com/@deanwball)
- [Anthropic has confirmed](https://red.anthropic.com/2026/mythos-preview/)
- [METR’s latest update](https://metr.org/time-horizons/)
- [Project Glasswing](https://www.anthropic.com/glasswing)
- [@jackclarkSF](https://x.com/@jackclarkSF)
- [Import AI](https://importai.substack.com/p/import-ai-455-automating-ai-research)
- [@karpathy](https://x.com/@karpathy)
- [autoresearch](https://github.com/karpathy/autoresearch)
- [$2.1 billion](https://www.isomorphiclabs.com/articles/isomorphic-labs-announces-series-b-investment-round)
- [2:38 AM · May 15, 2026](https://x.com/daniel_mac8/status/2054994899422826592)
- [10.9K Views](https://x.com/daniel_mac8/status/2054994899422826592/analytics)
- [View quotes](https://x.com/daniel_mac8/status/2054994899422826592/quotes)
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*导出时间: 2026/5/15 10:42:57*
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## 中文翻译
# 重磅事件正在发生。其规模超乎你想象。
**作者**: Dan McAteer
**日期**: 2026-05-14T18:38:58.000Z
**来源**: [https://x.com/daniel_mac8/status/2054994899422826592](https://x.com/daniel_mac8/status/2054994899422826592)
---
回想一下 2026 年 2 月。
@mattshumer_ 的一篇爆款文章《重磅事件正在发生》道出了一种感觉：AI 开始奏效了。AI 真的来了。就像新冠疫情爆发初期一样，我们当时并没有意识到这意味着什么。
几个月后，我们不再只有感觉。
我们亲眼见证了指数级的进步。
三个月前，证据还只是轶事传闻：软件工程师放了个假，一些知名人士发现 AI 编程代理真能干活。他们告诉了各自的受众，浪潮开始涌动。像 @deanwball 这样的聪明人开始说 Claude Code + Opus 4.5 就是通用人工智能（AGI）。
自那时起，证据已变得制度化：AISI、METR、与 Anthropic 关联的联盟、网络安全组织、巨额的 AI 科学融资。
如果说 Matt 的文章是“我们看到天上有不明飞行物（UFO）”，那么这篇文章就是：
> “外星人已经登陆。他们想见我们的领导人。”
# 智能体已成定局
最近有两项结果表明，智能体已在很大程度上实现了自主。
1. Claude Mythos 攻克 AISI 全套网络攻击模拟
Claude Mythos 预览版是首个成功完成人工智能安全研究所两项网络攻击模拟的模型。“最后的守护者”是 Mythos 在 10 次尝试中成功完成 6 次的网络测试之一。据估计，人类攻击者需要 20 小时才能完成。Mythos 完成的另一项名为“冷却塔”的网络攻击测试此前无人能解。Mythos 在 10 次尝试中成功 3 次。
2025 年 11 月，AISI 网络测试任务持续时间的近似倍增时间为 8 个月。到 2026 年 2 月，这一时间加速至约 4.7 个月。Mythos 和 GPT-5.5 都领先于 4.7 个月的这一趋势。
如果这一趋势保持下去，AI 任务持续时间（至少在网络任务方面）将呈现超指数级增长。模型能完成的任务正在变长，而且改进速度本身也在加快。
没有理由认为这种能力的飞跃仅限于网络任务。Anthropic 已证实 Mythos 是一个“新型通用语言模型”，并表示网络能力“是代码、推理和自主性普遍改进的下游衍生结果”。
Anthropic 决定首先向选定的组织提供 Mythos 用于网络防御目的，因为这对社会最为重要。这种程度的能力也应能推广到其他领域。没有理由认为它不会。
2. Mythos 击穿 METR
METR 衡量的是一个简单的东西：熟练人类完成一项任务需要多长时间，以及 AI 智能体能否以给定的成功率完成该时长的任务。他们的“50% 时间视界”是指智能体在需要人类专家耗时那么久的任务上成功一半的点。
Claude Mythos 预览版现在已经超出了该测量范围的边缘。METR 最新的更新加入了早期的 Mythos 结果，并警告说在当前任务套件中，超过 16 小时的测量是不可靠的。
之所以有趣，不仅仅是因为“Mythos 能做更长的任务”，还因为我们的 AI 模型现在正在超越我们对其进行基准测试的能力。我们开始耗尽那些足够长、干净且经过良好测量的任务，难以测试这些智能体到底能做什么。
在几年时间里（至少如此），AI 从“能做耗时人类几分钟的任务”变成了“能做耗时人类几天的任务”。METR 说测量结果已经不可靠了。
网络是最尖锐的现实世界测试案例，但 Anthropic 表示这种能力来自代码、推理和自主性的普遍改进。METR 的结果也证实了这一点。
智能体现在可以执行真正的、具有经济价值的工作。它们正在迅速变强。
> 智能体已变得不可否认。
如果好人有这东西，那是好事。如果坏人也有，那就没那么好了。
# 安全界做出反应
这正是 Anthropic 没有直接向所有人发布 Mythos 的原因。
相反，他们启动了“透明翼计划”：与 AWS、Apple、Broadcom、Cisco、CrowdStrike、Google、摩根大通、Linux 基金会、Microsoft、NVIDIA、Palo Alto Networks 等公司协调，首先将 Mythos 用于网络防御。
![Image](https://pbs.twimg.com/media/HITOklJXkAAi1Ld?format=jpg&name=medium
想一想这件事。
一家处于领先地位的 AI 实验室构建了一个通用模型，它在查找和利用软件漏洞方面能力如此之强，以至于他们认为负责任的首次发布不应是聊天机器人产品，而是一个安全联盟。一些人批评此举纯属“营销炒作”。但如果是为了营销策略，广泛发布 Mythos 并持续增加收入本来会是更好的选择。
Anthropic 表示 Mythos 已经发现了数千个高危漏洞，包括所有主要操作系统和所有主要网络浏览器中的错误。其中一些相当古老。一个是 OpenBSD 中长达 27 年的漏洞。另一个是 FFmpeg 中存在了 16 年的 bug，自动化测试工具曾数百万次触及其却未曾发现。
再次强调：Mythos 并非专门被训练成网络武器。网络能力是编码、推理和自主性改进的衍生结果。
这才是最让所有人不寒而栗的部分。
如果你拥有一个能够自主发现细微漏洞、将漏洞链接在一起、逆向工程二进制文件并生成可利用漏洞的 AI 智能体，你就拥有了一种可以改变攻防平衡的东西。
短期内，这是危险的。攻击者只需要找到一条入口。防御者必须防住一切。如果像 Mythos 这样的模型在防御者准备好之前变得广泛可用，坏人也会获得筹码。
但这正是上行空间如此巨大的原因。
好人可能第一次拥有了一种工具，可以搜索大量关键软件并在攻击者之前发现隐藏的裂缝。Anthropic 为“透明翼计划”和额外参与者提供了 1 亿美元的使用额度，此外还为开源安全组织提供了数百万美元。
这就是为什么将强大的 AI 引向正确的方向如此重要。
它可能真正成为社会的福音，但这只有在我们以此方式使用它时才行。
强大的 AI 可以帮助强化银行、医院、政府、浏览器、手机、服务器和开源项目所依赖的软件。
我预计这种模式会重复出现。使 AI 具有风险的能力同样使其具有用途。可能被滥用的自主性也可以用于防御、发现、修复和建设。
> 这与其说是一把双刃剑，不如说是一把双刃的认知核武器。
# 下一前沿是进行 AI 研究
而且网络并不是故事的终点。
这只是文明基础设施真正落地的第一个地方。
下一个前沿是 AI 系统自己进行 AI 研究。然后，可能是广义上的自主科学研究。
Anthropic 联合创始人兼《Import AI》作者 @jackclarkSF 最近表示，他认为到 2028 年底，我们没有人类参与的 AI 研发（R&D）发生的可能性为 60% 以上。通俗地说：一个 AI 系统足够强大，可以自主构建其继任者。
这听起来像科幻小说，直到你看看什么是 AI 研究以及模型已经擅长什么。
想一想。什么是 AI 研究？其中一部分是灵感。新想法。品味。奇怪的直觉。那种让人能从全新视角看问题的东西。
但大部分是别的东西。
很多 AI 研究是写代码、运行实验、清洗数据、读论文、复现结果、调试训练运行、优化内核、比较模型输出、搜索失败的尝试并重试。
这正是智能体所擅长的那种工作。
@karpathy 的自动研究项目证明了今天的 AI 模型已经可以进行自主研究以改进模型自身的超参数。当你不仅能改进模型的超参数，还能自主改进算法、架构、数据混合，以及所有使模型比现在更强的成分时，会发生什么？
这正是最新的 METR 结果发挥作用的地方。现在模型可以执行可能需要人类软件工程师几天才能完成的编码任务。很快，模型可能能够完成耗时人类研究人员更长时间的研究任务。
AI 还不能取代最好的人类研究人员。不是某种能按命令发明 Transformer 继任者的神奇神谕。
而是一个可以承担越来越多苦差事的合成同事。
一旦 AI 可以自动化大块 AI 研究，进步就不再仅仅局限于能够思考、编码、测试和迭代的人类研究人员的数量。进步开始受到算力、数据、评估、安全性以及我们指挥这些系统的程度的限制。
然后人类研究人员可以做他们最擅长的事情：坐着寻找灵感。自主 AI 研究人员可以做机器最擅长的事情：不知疲倦地执行苦差事。
因为如果 AI 有助于构建更好的 AI，而更好的 AI 又有助于构建甚至更好的 AI，反馈循环就会变得递归。正在改进的东西也成为了改进过程的一部分。
这就是为什么“发生了一件大事”变成了“它比你想象的更大”。人类很难概念化指数级的进步。这不是我们进化出来的方式。
由于我们固有的认知限制，AI 的进步可能会在字面上加速超过我们可以想象的程度。
如果 AI 可以自动化自己的研究，它最终自动化整个科学研究也是合理的。它可能能够自主创建解释性的科学知识。它可以 24/7/365 地做这件事。在 AI 能够在原子空间运行实验之前，这很难，但这是合理的。
量子物理学家和科学哲学家 David Deutsch 将真正的财富定义为“你拥有知识可以在世界上实现的物理转化集合”。
强大的 AI 可能会达到可以自主创造这种财富的地步，只要我们保持数据中心运行，就会无休止地创造。
# 上行空间不仅仅是生产力
这就是为什么上行空间远大于“AI 让工人更有效率”。
这种框架是正确的，但太小了。它让 AI 听起来像是更好的电子邮件、更快的电子表格、更便宜的客户支持和更快的软件工程师。有用。有价值。在经济上很重要。
但不是具有世界历史意义的。
真正的上行空间不是 AI 让我们更快地做同样的事情。而是 AI 可能让我们做我们今天根本做不到的事情。
上行空间是 AI 为人类开辟了新的视野。
它可能让我们搜索人类团队过于庞大的空间。阅读任何研究者都无法独自读完的文献。运行模拟、提出实验、调试失败、生成假设、编写代码、比较结果，并在实验室里的每个人都回家后继续工作。
这比生产力更重要。
这是发现的加速。
一个具体的例子是药物发现。Isomorphic Labs 最近筹集了 21 亿美元，用于构建设计新药的 AI 系统。这并不意味着 AI 已经治愈了疾病。并不意味着生物学已解决。生物学混乱、昂贵、物理性，且极度让人谦卑。
但这确实意味着世界上一些最聪明的资本现在正在押注 AI 可以改变药物发现的经济学。
Isomorphic 声明的使命是“解决所有疾病”。
那将是件大事。
因为 AI 的乐观情况不是“你的老板得到了一个更便宜的实习生”。乐观的情况是，人类在对抗癌症、阿尔茨海默病、罕见病、抗生素耐药性、衰老、气候、材料科学、能源以及所有其他进步受到我们创造和测试知识速度限制的难题时，获得更多的尝试机会。
我们中的许多人仍然将 AI 视为一种劳动技术。一种生产力工具。
我们问：它取代了谁的工作？它压缩了谁的薪水？哪家公司变得更有效率？
这些都是重要的问题。我们应该认真对待它们。会有颠覆。会有输家。会有在过渡中被困住的人，他们并没有要求成为文明技术实验的一部分。这可能是我们大多数人。我们不应该对此轻描淡写。
但劳动并不是这里最深的类别。
知识才是。
如果 AI 成为创造新知识的系统，那么相关的比较就不是外包或自动化。而是印刷机。科学方法。计算机。互联网。也许比所有这些都更大，因为这一次工具不仅仅是存储或传输人类思想。它开始参与生成它的过程。
它将成为一种能够创造新知识的力量。在整个历史上，人类一直是这种力量的唯一已知例子。
这就是为什么这一时刻：
> 比你想象的更大。
可以发现软件漏洞的相同系统可能有助于保护医院和银行。可以自动化部分 AI 研究的相同系统可能有助于自动化生物学、化学、材料科学、工程学和医学的部分工作。可以取代某些工作形式的相同系统也可能为数百万人提供以前无论如何都无法获得的家庭教师、教练、合作者、翻译、程序员、分析师和教师。
想象一个世界，小镇上的孩子可以通过一个无限耐心的、针对他们个人调整的家庭教师学习数学、编程、物理、写作、生物学、设计和创业。
想象一名护士转行做软件。一名工人学习机器人技术。一名创始人在构建一个五年前需要二十人工程团队的产品。一名罕见病患者获得 AI 辅助的最新研究解释，而他们的当地医生永远没有时间阅读这些内容。
这一切都不是必然的。
那部分很重要。
强大的 AI 不会自动公平地分配自己。它不会自动治愈疾病。它不会自动让人们更明智、机构更好或社会更人性化。它可以集中权力。它可以加速冲突。它可以扩大不平等。它可以用合成的说服力和自动化的进攻淹没世界。
但是，看着一种可能自动化科学大部分工作的技术却只看到危险，这也是不现实的。
危险是真实的。上行空间也是真实的。
而且上行空间不是边缘性的。
如果我们能将这些系统引向正确的方向，它们可能成为文明比问题复合得更快解决问题的一种方式。它们可能帮助我们强化数字世界、发现新药、发明新材料、教授新技能、建立新机构，并创造不仅是金融的，而且是物理的、科学的、教育的和人类的财富形式。
> 它们可能帮助我们推迟死亡。它们可能帮助我们扩展生命。
这就是我认为人们仍然低估的“发生了一件大事”的版本。
比更智能的模型更大。
比更好的编码代理更大。
比带有聊天框的生产力软件更大。
一台创造知识的新引擎。
这就是为什么我们必须做对。
# 危险是真实的。它们不是故事的全部。
这就是公众对话通常崩溃的地方。正因如此，这是我们需要走到一起的地方。
一方看到了上行空间，将风险视为讨厌的脚注。T