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Anton Leicht

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《认知革命》节目嘉宾:《AI权力的平衡:安东·莱希特谈政治、把控交易节奏与妥善应对》。

1 场访谈 · 5 条观点 · 3 个话题

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按访谈日期排序的归属观点。这是相关对话的快照,而非对某人信念的最终定论。

AI安全

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AI安全

部分灾难性情景缺乏渐进式市场预警

安东·莱希特对“灾难性AI风险会产生一系列平滑且可交易的市场预警”这一假设提出质疑。他表示,许多情景描述的是战略和经济状况看起来良好,直到发生接管或重大事件;并称自己对此类可能性没有好的交易策略。

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原始摘录

I think there's a very reasonable doomer view that concentrates basically all of the probability mass of doom on things going well all the way until doom. If you look at a lot of the canonical doom scenarios — people who've talked seriously about existential and catastrophic risk — many of them describe a scenario where everything looks like it's going really well, strategically and economically great, until the point where the takeover happens

翻译 · 非原文措辞

我认为有一种非常合理的末日论观点,它基本上把末日的所有概率质量都集中在“一切进展顺利直到末日降临”这一情形上。如果你看看许多经典的末日情景——那些严肃讨论过生存性与灾难性风险的人——其中许多人描述的情景是:在战略和经济上一切看起来都非常好、非常出色,直到接管发生的那一刻。

安东·莱希特谈AI部署、经济颠覆与风险
上下文

是的——再次回到“走钢丝”这个比喻:这次对话非常精彩。最后来个快问快答怎么样?你提到了对冲,还提到在挪威主权财富基金的背景下,买入合适的股票以成功度过AI转型期。最近我一直在纠结的一个挑战是Tyler Cowen的挑战:如果你这么悲观,你的做空标的是什么?我一直在努力给出一个切实的答案——我不是彻底的末日论者,但我认为他应该比现在更严肃地对待这个问题。所以我希望得到的回答要么是“这是我的做空标的”,要么是“我真的试过了,但想不出任何东西”,而这正是我现在的状态——我想不出在末日情景中致富的方法。你有什么建议可以回答Tyler吗?我也认为许多人只是持有逻辑自洽的世界观,却并不据此下注——即使他们深信不疑,也不会进行金融押注。这是一个更无趣的元层面论点。但我的主要观点是,我不认为这是一个平滑的概率分布。我认为很多事情要么进展得非常顺利,要么变得非常糟糕,并没有多少世界是波动不定、先变糟一阵子再变好一阵子的。所以遗憾的是,我也没有好的交易策略。

原始上下文

Yeah — again, to talk about "threading the needle": this has been awesome. How about a little lightning round to close? You mentioned hedging, and also, in the context of Norway's sovereign wealth fund, buying the right equities to get through the AI transition successfully. A challenge I've been wrestling with lately is the Tyler Cowen challenge: if you're so doomer, what are your shorts? I've been trying to come up with an actual answer to that — I'm not a total doomer, but I think he should be taking it more seriously than he is. So I want an answer that's either "here are my shorts" or "I really tried and I can't come up with anything," and that's kind of where I'm at right now — I cannot come up with a way to get rich in the doom scenario. Do you have any suggestions to answer Tyler? I also think many people just have coherent worldviews that they don't bet on — they don't take financial bets even when they're committed to them. That's a more boring meta-contention. But my main view is that I don't think it's a smooth distribution of probabilities. I think a lot of this either goes very well or goes very badly, and there's not a lot of world where it's volatile and goes kind of badly for a while and then kind of well for a while. So, sadly, I don't have a good trading strategy either.

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AI安全

自主智能体出现时间早于预期

安东·莱希特表示,高度自主且可能目标错位的智能体在能力发展轨迹上出现的时间早于人们的预期。他将此与早期主要将生物风险视为人类滥用风险的框架进行了对比。

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原始摘录

very autonomous, and potentially somewhat malicious or at least misaligned, agents have come much earlier in the capability trajectory than people expected. Relative to what the agents can actually do, they're sort of out of control earlier than people might have thought.

翻译 · 非原文措辞

高度自主、且潜在地具有某种恶意或至少目标错位的智能体,在能力发展轨迹上出现的时间远早于人们的预期。相对于这些智能体实际能做的事情而言,它们失控的时间点比人们原本设想的要早。

安东·莱希特谈AI部署、经济颠覆与风险
上下文

其中有一个案例尤其如此;坦白讲,我甚至不确定当前模型在该领域是否已经相当危险。我透过我们目前极为有限的观察窗口审视OpenFace事件时注意到,最早使用留言板的智能体之一正在处理的任务竟与蛋白质数据库有关。这让我感到不安,因为我想,等一下——这意味着当它们接入留言板时,生物专家和网络专家是在同一个环境中,或者至少在同一个环境中进行交叉训练——它们以某种交叉污染的方式运行着同样的评估测试。我们已经看到智能体突破系统边界。我们已有在真实世界中针对真人实施社会工程攻击的存在性证明。而我只能想,我不知道——现在还有谁能确信它们做不到这一点?专家们似乎很自信,但我的元观察是,专家们眼下似乎经常感到意外。这很有意思,因为过去人们常以“现实世界存在诸多瓶颈”来回应生物风险论点——我仍然认为这些瓶颈确实存在,这也让我的担忧程度略低于你。而且我认为,如果我没记错的话,Helen曾就此向你提出过一些很好的回应——围绕云实验室的整合程度、以及现实中究竟能做到什么程度,双方本可展开一场有价值的讨论。我认为这既适用于智能体失控问题,也适用于滥用问题。

原始上下文

one in particular, and I am honestly not even sure at this point that the current models aren't perhaps quite dangerous in that domain. I squint through the limited peephole we have at the OpenFace incident, and I noticed that one of the tasks one of the earliest agents to ever use the message board was working on was something related to a protein database. That kind of freaked me out, because I was like, wait a second — that means they're cross-training in the same environment, or at least in the same environment when they have the message board, these bio and cyber specialists — they're running these same evals, at least in a kind of cross-contaminated way. You've got agents breaking out. We've got existence proofs of social engineering in the wild against real people. And I'm just like, I don't know — should anyone be confident that they can't do that at this point? The experts seem to be confident, but my meta-observation is the experts seem to be surprised quite often right now. And so it's interesting, because people used to respond to the bio argument by saying, well, there are a lot of real-world bottlenecks — which I do still think exist, which makes me a little less worried than you. And I think, if I remember correctly, Helen had some good responses to you on that — there's a good back-and-forth to be had around how integrated the cloud labs are, how much you can actually do in the real world. I think that applies both to loss of control over agents and to misuse.

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企业级AI

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企业级AI

AI整合需要组织架构重构

安东·莱希特指出,与专有数据和工作流的整合以及组织架构的重构,是使用现有AI能力的瓶颈。他预计团队构成和生产力将受到颠覆性影响,同时对短期内的岗位替代情况表示不确定,因为AI也可能创造额外的需求和工作。

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原始摘录

I think it's integrating with proprietary data, integrating with proprietary workflows. Obviously not the entire task profile of humans is currently covered by models, but on specific tasks they're better than humans, and you can drop them into specific task profiles — at least in software engineering, and also in some other general white-collar activities. I think the question is just that the labor market takes its time to rearrange around that

翻译 · 非原文措辞

我认为是与专有数据集成,与专有工作流集成。显然,当前模型尚未覆盖人类的全部任务范畴,但在特定任务上它们优于人类,你可以把它们放入特定的任务环节——至少在软件工程中是这样,在其他一些通用白领活动中也是如此。我认为问题只是劳动力市场需要时间来围绕这一点重新调整。

安东·莱希特谈AI部署、经济颠覆与风险
上下文

一个更贴近现实的关切。我们之前谈过一些瓶颈——我指的不是人类因惯性而迟迟未能像理论上本可那样大规模采用AI的现象。但如果反过来只看AI及其能力,相较于雇佣真人完成工作的体验,显然缺失了某些东西,对吧?这种感觉一直越来越单薄——几乎到了我现在很难明确指出Fable 5.1或Astra究竟在哪些方面不如雇佣真人的地步。你能否解答:这种差距究竟是什么?还需要哪些额外的边际能力提升才能真正造成劳动力市场的颠覆?你认为在一个假设的世界里会发生什么:特斯拉决定授权其全自动驾驶系统,并且在大约18个月内——假设我们把它列为优先事项,所以这里有点虚构情景——但突然之间基本上所有汽车都能自动驾驶,而那大约四百万以此为生的美国人不再需要开车了。这似乎是一个非常清晰的替代故事,而且极有可能发生。你认为经济能吸纳这些人吗?他们去哪里?仔细想想真的很难——好吧,这个人开了25年卡车,还没到退休年龄,但卡车现在能自己开了。他会怎样?

原始上下文

A more terrestrial concern. We talked a bit about bottlenecks — I don't mean the human inertia around why adoption hasn't happened as much as it obviously could, in theory, have happened so far. But if you take the flip side of that and just look at the AIs and their capabilities, clearly there's something missing relative to the experience of hiring a human to do work, right? It feels thinner and thinner all the time — almost to the point where I'm now having a hard time putting my finger on what it is about Fable 5.1 or Astra that's actually worse than hiring a human. Do you have an answer for what that is, and what additional marginal capability gain you'd expect to actually create labor market disruption? What do you think would happen in a hypothetical world where Tesla decides to license its full self-driving, and within 18 months or so — let's say we make a priority of it, so we're into a bit of a fictional scenario here — but all of a sudden basically all the cars drive themselves, and the four or so million Americans who make their living driving aren't needed to drive anymore. That seems like a pretty clear displacement story that very well could happen. Do you think the economy can absorb those people? Where do they go? It seems really tough when you get down to it — okay, this guy's driven a truck for 25 years, he's not ready to retire, but the truck now drives itself. What happens to him?

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AI与经济

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AI与经济

经济部署滞后于当前模型能力

安东·莱希特认同当前AI能力向经济的部署大幅滞后于这些能力本身。他提出,如果将现有的西方算力转向推理,其生产性用途可以证明迄今在前沿模型和芯片上的投资是合理的。

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原始摘录

we have a big lag in terms of deploying even the current level of capabilities to the economy. And I think if we switched all the compute that exists in the West right now to only inference, we'd find economic, productive applications for all the models that would allow us to justify the investment on all the frontier models

翻译 · 非原文措辞

我们在将当前水平的能力部署到经济中的过程中存在很大的滞后。我认为,如果我们把西方现有的全部算力都切换到只做推理,我们会为所有这些模型找到具备经济与生产价值的应用场景,从而让我们能够证明对所有前沿模型的投资是合理的。

安东·莱希特谈AI部署、经济颠覆与风险
上下文

那么股市呢?我想就此再追问一点。我目前的理论是,暂停实际上对股市未必有那么糟,因为模型已经足够聪明,真正制约需求的不是它们的能力,而是人类有效部署它们的能力。所以它们在千禧年大奖难题上的成功率是10%还是30%其实并不重要——更关键的是,会计部门的Bob能做什么,才能一个人完成两个人的工作量?你怎么看?我认为相关公司的估值——无论是未上市公司,还是AI供应链中的上市公司——很可能建立在我们能做到远超于此的预期之上。它们或许并未完全陷入AGI狂热,但我确实认为,其估值所预期的每GPU推理价格(或不管那是什么),远高于让会计部门的Bob为Fable 5.1付费所合理的价格。如果你想合理解释这些估值,如果你想合理解释建设的规模、你所预期的合同形态和需求形态,那它更像是与制药和材料科学等领域签订数百万乃至数千万美元的研发加速合同,在这些领域AI能做出重大贡献并帮助你发现极其有利可图的新药——诸如此类。你可能还预期会有进一步的内部效能提升,以及真正具备市场竞争力的自动化AI研发用途——能够索要高得多的价格的编程智能体。

原始上下文

How about the stock market? I wanted to do one follow-up on that. My theory right now is that a pause wouldn't actually be that bad for the stock market, because the models are smart enough that demand isn't really limited by their capability, but by human ability to deploy them effectively. So it doesn't really matter if they succeed 10% of the time or 30% of the time on Millennium Prize problems — it's much more like, what can Bob in accounting do to get two people's worth of work done as one person? What do you think? I think the valuations of the companies — both the non-IPO companies and the publicly listed companies that are in the AI supply chain — probably rest on us doing more than that. They're probably not entirely AGI-pilled, but I do think they expect a per-GPU inference price, or whatever that is, that's a lot higher than what would make sense for Bob from accounting to pay for even Fable 5.1. If you want to make sense of the valuations, and if you want to make sense of the scale of the build-out, the shape of contract and the shape of demand you're expecting is more like millions and millions of dollars in R&D acceleration contracts with pharma and material science and so on, where they can make major contributions and help you find extremely profitable new drugs — that kind of thing. You're probably also expecting further internal uplift and actually competitively priced automated AI R&D uses — coding agents that are able to ask for much, much higher prices.

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AI与经济

先进AI可能挑战经济追赶机制

安东·莱希特认为,近几十年来中低收入国家所使用的许多经济追赶机制,与一个拥有先进AI和日益自动化制造业的世界严重不相容。

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原始摘录

fundamentally, a lot of the catch-up mechanisms that lower-middle-income countries, in very general terms, have used and enjoyed and been able to leverage over the last few decades are just deeply incompatible with a world that has both very advanced AI systems and, downstream of that, ultimately much more automated manufacturing capacity.

翻译 · 非原文措辞

从根本上说,在过去几十年里,中下等收入国家普遍使用、受益并得以利用的许多追赶机制,在一个既拥有非常先进的AI系统、又由此最终拥有高度自动化制造能力的世界里,是完全不兼容的。

安东·莱希特谈AI部署、经济颠覆与风险
上下文

那么我们来谈谈世界其他地区。你刚发布了一份关于欧洲变革性AI战略的重要报告,此前也引发了一些讨论——我之前还曾与你们的一位合著者聊过欧洲面临的算力赤字问题,以及为确保未来持续参与竞争而需要采取的行动。但在深入探讨欧洲应如何制定战略之前——如果他们什么都不做,令人担忧的是什么?因为我也认为,无论你认为欧洲维持现状会面临什么,这可能就是全球约70%、也许是80%的人口默认会面临的处境。在你看来,非洲、拉丁美洲、南亚等地的未来图景是什么样的?一直以来最直接、最显见的机制就是押注人口结构差异——你有非常快的人口增长,有相当廉价的劳动力可以用来发挥比较优势,然后迅速启动,吸引一些外国企业入驻,向全球供应链出口一些有价值的商品,其前提是你拥有这样一支劳动力队伍,能以一种使你成为开展商业活动相对有利的国家的方式加以利用。而我只是不知道这种情况是否还会继续。对于低端服务经济的绝大多数方面,这肯定不会再继续了——一旦我们拥有非常强大的AI系统,我实在看不出该经济部门还能以何种稳定的方式存在。肯定会出现新的服务类工作,即人类偏好型工作——你可以从长远角度思考所有这些劳动力市场效应。但这种仅凭……就能立即对全球供应链有用的想法

原始上下文

So let's talk about the rest of the world. You have this big report that just came out on transformative AI strategy for Europe, and there's been some discussion — I actually talked to one of your coauthors a bit back about the compute deficit that Europe has and the need to do something to be a live player going forward. But before we get into the strategy for what Europe should do — what's the worry if they do nothing? Because I also think whatever you think might happen to Europe if Europe stays the course is probably what happens to, like, 70%, maybe 80%, of the world's population by default. What does the future look like in your mind for Africa, Latin America, South Asia, etcetera? So the most immediate and obvious mechanism was always just to bet on the demographic differences — you had very rapid population growth, a fairly cheap workforce you'd be able to use to your comparative advantage, and then quickly bootstrap into hosting some foreign firms and exporting some valuable good into the global supply chain, predicated on the idea that you had this workforce you could put to use in a way that made you a comparatively beneficial country to conduct business activity in. And I just don't know whether that's going to remain the case. It's definitely not going to remain the case for most aspects of the menial services economy — I just don't see a stable way that that sector of the economy really exists once we have very powerful AI systems. There are definitely going to be new services jobs, human-preference jobs — you can think about all these labor-market effects in the long run. But this idea that you can just be immediately useful to global supply chains by do

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