# 46 thoughts on the near future
**作者**: bayes
**日期**: 2026-06-30T20:37:30.000Z
**来源**: [https://x.com/bayeslord/status/2072056960430789032](https://x.com/bayeslord/status/2072056960430789032)
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This list is based on a thread I posted on June 4th. A few edits and additions here and there. Several people asked me to make the thread easier to read, so here it is.
## Intelligence
1. I think people are going to be blindsided by algorithmic progress. The entire world, markets, governments, militaries, companies, people, etc. are all trying to make sense of AI and its impact in terms of the recent past’s production efficiencies and regularities, and how things appear to be going. Even several of the purportedly “RSI”pilled neolabs seem to think this will be business as usual but with Agent in a loop. No. My guess is there are many algorithmic OOMs left to go in the production of intelligence, maybe (maybe) up to ten, with four to seven seeming more likely. Going beyond even ten is possible in principle, but it strains hard against what I suspect the universe will actually let us do. Implausible but not impossible. If this is true then things aren’t actually going as they appear to be going and a big jump is coming. Anything along these lines happening would make things, far weirder than almost anyone seems to be pricing in.
2. We are in early takeoff. AI improving AI may end up being one of the most consequential steps of history. This isn’t certain because we don’t know how far from the physical and computational limits of intelligence we are, though I would bet it’s quite far from where we are today (as I said above, ~4-10 OOMs more intelligence output per unit of scale seems possible).
3. Now that we’re in takeoff, algorithmic research is accelerating. Compute is still a scarce resource, but researcher-time opportunity costs are lower because you can just send an agent on any quest or wild goose chase. It might come back with something. All new ideas come with optimization debt that can now be paid in unsupervised token spend. Vast numbers of research scaling law curves will be traversed.
4. AI models, especially the frontier, will keep getting better. The only true wall is physics. Models are increasingly autonomous, smart, and are getting better all the time. Math and code are falling to scale+RL, everything else is up next. Verifiable vs. non-verifiable as a meaningful distinction will fade. Automated AI research and AI learning are going to look more and more related as we go forward. Training models well is closely related to models learning well in general. Sample efficiency, creativity, and all other limitations will be solved and then start approaching algorithmic optimality at whatever scale.
5. The idea that long horizon agents always need equivalently long horizon training is wrong because generalization in time exists. Long tasks are not made of longness! This is related to LeCun’s fallacy of (1-e)^n error accumulation. What’s actually going on is error correction. This happens at multiple scales from the single token generation level up to steps in a long task. Part of the reason the METR graph goes up is that agents are starting to hit error correction escape velocity.
6. An engineering-grade science of deep learning is imminent. This will drive us to AI algorithmic maturity much more rapidly than people are expecting, though as I mentioned above it’s not clear how far this can go even in principle. For example, a science of scale-invariance dramatically increases the scale and returns of useful experimentation because experiments on one GPU can tell you how to use one hundred thousand.
7. There will be Move 37 moments for every domain of technical human endeavor and then, quite quickly, Move 37s will seem quaint. I mean for everything.
8. Compute is going to keep improving. Today’s best matmul machines are nowhere near the physical limits of AI accelerators. There’s a lot of room to get better at digital silicon. There are also many candidates for new substrates, and the algorithmic debt they owe will be automated to its limits, but we don’t yet know what the optimal one is for AI in space/energy/time/manufacturability/cost. Photonics and stochastic silicon are both interesting candidates, but I also expect the singularity to be surprising.
9. How far ahead the labs can get depends in part on the returns to automation and scale, which includes the returns to greater algorithmic depth. If deep learning practice (and theory) is forever shallow then the moat will mostly not be algorithmic on the longer term because secrets will be relatively cheap to discover. Eventually distillation + data + time can catch up to compute scale, potentially slowly. So far this seems partly where we’re at, but even if true there are no guarantees it will continue this way.
10. If things become less shallow as we scale then every increment of automation and scale buy you algorithmic secrets that are increasingly out of reach for anyone else. This too seems partly where we’re at. The end point in either case is when marginal utility returns to scale and research saturate. We don’t know where that is. Could be 2 OOMs or 20 away from where we are today. No one knows.
## The intelligence supply chain
1. Compute will be a highly contested resource for at least a few years. But in that time it will start commoditizing and we will laugh at the impoverished 2020s. Scale is increasing and working, capital is following to turn the wheel again and again. More matmul machines, more fabs and more energy are coming. Bottlenecks of intelligence production are temporary. Potential economic speed bumps notwithstanding.
2. The nature of the intelligence supply chain is changing. Right now it’s very centralized around labs. But labs are automating the main thing that makes them good: researchers and the discovery of algorithmic advantages. Once this starts happening, assuming open source trails not too far behind, and especially if the labs don’t lock down AI researcher models, the labs’ advantages will come from easier capital, having more compute, having special data, business relationships, and good products. This does depend on how the algorithmic depth point above resolves, among other things.
3. Distributed training will reduce the need for monolithic datacenter buildouts, offering some advantage to non-hyperscalers. This won’t outpace hyperscalers in pure single largest run scale terms, though.
4. Automated AI experimentation will enable widespread discovery of algorithmic secrets as these are naturally more distributable than full-scale training runs. It’s unclear how far this can go but I expect pretty far. As mentioned above the fundamental depth of deep learning is still unknown and the upper bounds on this point depends on that.
5. It’s possible that despite these forces apparently in its favor academic and open source will languish because of the cost and opportunity cost of compute. E.g. are GB300s more valuable serving GLM5.2, or Fable? Is it more valuable doing non-frontier research in some academic lab or building Mythos 2 inside of Anthropic? The market will solve for where demand is greatest, which right now does seem to be the labs. This means that open source labs could become even more compute starved *even if they have capital*, if they don’t already have compute capacity locked in. And even then they will be calculating opportunity cost of their research vs renting. See Colossus x Anthropic.
6. Open source may also begin to have a hard time socially in an environment where AI capabilities start getting spicier (in the next 0-18 months), particularly assuming we are slow to accelerate security, which we have been so far.
7. Open source might begin to languish as capital rushes into the labs. There is a coordination problem here where no one wants a token monopoly except the labs (and maybe the government), but if that can get solved and the regulatory environment is favorable maybe things work out.
## Robotics
1. There will be a ChatGPT style November 2022 moment, and then an Opus 4.5 style November 2025 moment for robotics. Neither has happened yet, but they’re coming and it will happen faster than people think as a function of fast AI progress, including AI-accelerated physical systems engineering. Seems likely that the gap between these two moments for robotics will not be three years.
2. To physically scale up the count of robots in the world, however, might take until 2030 or later. Although we do build ~100M cars per year and humanoids are much smaller than cars. Given that we also build 1B smartphones per year it seems reasonable to expect order of 100M robots/yr by 2030 if capital and algorithms move quickly. Definitely 10M/yr is achievable as we already do that for the drone market. Good software proving that humanoids are worth it at small scale can drive infinite capital, proportional to the quality of the proof.
3. Things that might look like hard limits today for robotics will disappear, including e.g. poor sample efficiency, relative data scarcity, expensive and or challenging hardware designs for hands and motors, fractal complexity of the physical world, and hidden unrecorded knowledge about how we do things in the world (like plumbing). World models seem useful but the particular thing doesn’t matter. The research scaling laws will be ground out until utility diminishes.
4. Global demand for robots is easily in the tens of billions of units, especially if we sum over form factors. There is so much physical work worth automating. The market will try to solve for this and people will probably not get in the way.
## Progress
1. Science is automating and virtualizing. This means much of the progress we need in the world is going to come from automated labs and simulations. We don’t know the full computational limits of virtualization, but such robotically-driven labs for biology, materials science, and more are going to remove a large number of the bottlenecks, and along the way they will push the limits of validated virtualization to increase sample efficiency and the net returns to reification. Basically in every area we will have some combination of neural models, explicit simulations, and real world experiments all contributing to improving the returns per dollar and per time in areas like biology, materials science, and the like.
2. There are progress laws everywhere. In deep learning they are called scaling laws. It’s hard to tell when the S-curve saturation will happen on any given line, it’s hard to tell when there are new S-curves just over the horizon. The thing to understand here is that the engine of civilizational progress itself has a progress law. Most likely our progress will be of the saturating type like most natural processes we observe, but we actually don’t know where that happens. Technological and civilizational maturity could be close or far. We are (a) in the part of history where we’ve barely put any resources to progress but that is rapidly changing, and (b) we are automating the machine that directly outputs more progress. Ours are interesting times.
3. Scale up vs scale out futures. Zero to one vs. one to n. How much progress in breadth and depth the universe will allow us to have is an open question. Breadth is easier to estimate because it’s something like “How many total steps of computation will the laws of physics let us do from here on?”. How “deep” that computation can be, in the generic sense of the word, is unknown. There are versions of the future where the tech tree is so deep and the reachable computational universe is so rich that we will just keep inventing and discovering and inventing until physics stops us, if it ever does. Other versions are flatter; we max out a shallower possible tech tree soon and reach technological maturity relatively easily, which we then scale out, again until contentment or physics stops us.
## Capital and Production
1. More capital and more intelligence means an intensified capitalism which means we drive to market equilibriums faster. Over time this naturally should imply deflation and competition to epsilon marginal cost for most important goods, including AI, food, housing, medicine, electronics, entertainment, and travel. This is assuming we don’t let people get in the way. They probably will in some cases.
2. Mining will be automated. Shipping, land, sea and air, will be automated. Factories will be automated. Factory workers will be automated. Distribution centers will be automated. The maintenance, improvement and scaling of the entire supply chain will be automated.
3. There will be humans with jobs for a long, long time. What percentage of humanity that will be is an open question. The people who claim the number will be high are overconfident, as are the people who claim the number will be zero. It does seem hard to imagine how humans will contribute on the margin to the knowledge part of knowledge work for much longer. Demand for some things, like doctors, might go down a lot if we have superhuman AI doctors for $20/month + a la carte testing + significantly improved health via better medical technology. However because we cartelize doctors now, we might keep doing it and being a doctor will remain a great profession. Demand for entertainment will probably increase, but the cost of production will go down and the technical need for humans in entertainment has already decreased significantly. However we care a lot about other humans, so maybe we’ll keep caring about them and being an actor will become more lucrative. One way to think about how this might shape up is how many intermediate layers there are in the supply chain between a worker of today and the consumer. For a TikTok influencer there are zero layers. For a doctor, there are zero. For a factory worker there are many. The extent to which a job (a) can be disintermediated, or (b) can be outcompeted or (c) is fungible will probably determine a lot of their outcome. This analysis is quite subtle and this paragraph is not going to do it justice, but the last thing to mention is that this assumes we don’t have precipitous demand side collapse, which could happen if too many people don’t work and productivity/government efficiency isn’t good enough for UBI/UHI.
4. Related to but not in contradiction to the above points: the “permanent underclass” could be a real thing. In better worlds where it’s real it may look more like highly limited agency rather than detrimentally restricted income. For most people this will ultimately be fine, our agency is already highly restricted by modern society, but it will require psychological adaptation which might take time and could be painful.
## Culture and Psychology
1. The human psyche is slow to grow and adapt right now but this will change. The key thing will be to change in ways that are good, which may not be easy for some people. As a result of abundant intelligence and automation we are going to engineer durable psychologies much better than the unfit-for-our-environment evolutionary hangover we have today. There will be a thousand years of innovation in psychiatry and psychology in no more than decades. Humans will be fundamentally well. Crude, degenerate wireheading is overrated as a risk because there will be much more skillful and varied mind engineering available to us.
2. In a world of intense uncertainty people will race for power, status, wealth more intensely than ever, and in the process will happily defect against their fellow man. They will invent all sorts of justifications for why their behavior is good, even great. Look around.
3. You will live to see cringe you can’t believe.
4. There is a certain obvious doublespeak going on right now where those who stand to be, or already are, top 0.01% wealthy say that AI will benefit everyone, don’t worry about jobs, etc., but then they also wouldn’t give up their wealth to live as a random person on Earth, or even in America, in one year, five years, or twenty. People can see this and are already beginning to react. To be clear I wouldn’t give up my position either, but I’m also not saying everything will be perfectly fine (and I’m also not top 0.01% wealthy). As a result we are at risk of building an unjust world. Some people care about this and I think it should be discussed more frequently. And to be perfectly clear, American politics is abysmal in its way of addressing this sort of concern.
5. Elon seems likely to be the first quadrillionaire. Broadly speaking it’s not hard to imagine >> 1000x demand for more chips, robots, and spaceships, which he can probably capture a lot of.
## Coordination
1. The need for better coordination at all scales of society is obvious. There are weaknesses and risks to better coordination as we currently understand it, but it seems likely we’ve hardly scratched the surface of what’s possible. Could there be a Satoshi for defeating Moloch?
2. At least some international coordination on AI is likely a good idea. We may want treaties and GPU counting. This can be designed to (a) slow spiraling adversarial military and governmental power accumulation and (b) to have minimal impact on science and other important areas of progress. We may not get this because the GPUs are too broadly powerful. We got it for nukes because no one except the insane actually want to use nukes.
3. An AI lab coordinated pause or slowdown of AI production seems more likely than it was in 2023. Lots of tradeoffs here but I think the arguable value of a pause is slightly greater today than it was in 2023. The argument that it will be squandered is harder to make when we have automated research, which we don’t quite have yet (we have automated engineering). For what it’s worth I’m not personally in favor of a pause at this time, mostly because it breaks too many other parts of the tight rope walk through the singularity, the tech tree might have dragons, and adversaries are real.
## Power, violence, security, liberty
1. I regret to inform you that our universe might be vulnerable in the Bostromian sense. It’s possible that the current world has degrees of freedom that we can’t coordinate on controlling quickly enough while also having a continuation of the norms of the governance and liberty that are sufficient for the truth of our world, other than a panopticon. Note that in such worlds power accumulation is a slippery slope. A lot of these worlds probably end up sucking for most people. It would be nice if it weren’t true, but it might be true.
2. AI diffusion will happen at some speed greater than zero regardless of various potential rate-limiting factors. There are a lot of computers in the world and FLOPs to intelligence exchange rate is the lowest it will ever be. Don’t bet on things coming to a standstill.
3. The idea of a permanent underclass implies the existence of a permanent overclass. This presupposes people with more rights, for some relatively unjustified reason. The ultimate reason is always implied or realized violence-backed domination. But perhaps a world with advanced AI is a world with humans that have no justifiable rights to govern, no agreed-upon merit or standing beyond anyone other humans. This isn’t going to ever be 100% true but it might become more important to think about. I suspect the moral and practical cases diverge in practice quite a bit here, perhaps rightly so.
4. Institutions will be under pressure to transform from all directions, and those forces might lead to tyranny. There are many paths to get there, some through the guise of safety, some through benign power-creep where the ceiling is powerful AI+fully automated military supply chain+fully automated weapons. We need better institutions.
5. There could be a lot of zero days out there. In cyber, bio, infra, neuro, memetics, physics. We simply don’t understand the returns to algorithmic depth and coherence in these domains, both on the side of defense and robustness, and on the side of destruction. The algorithmic depth of nukes wasn’t out of reach for the world’s smartest humans. Tomorrow our machines will reach the next rung, and the next. Right now we know something about the stochastic catastrophe rates of an algorithmically shallow nature, and almost nothing about what happens in an algorithmically deep civilization.
6. Related: there could be some really fucked up stuff in the tech tree. We really don’t know.
7. Robotics capability at scale presents real takeover and coup-style risks above computer-based models, as well as more mundane things like new surface area and vectors for cyberattacks. We should take these risks seriously and work to reduce them.
8. Mutually assured destruction is based on 20th and early 21st century technology. We are going to undergo rapid technology change, maybe a millennium’s worth, in a short period of time. This means MAD is not a given. This is solvable and not a perfectly certain or clean disruption because error rate tolerance for decisive advantage is very low and potentially infeasible. Some people have brought this topic up in the past quite an unserious way, and I think that was wrong, and irresponsible. This is one of the most serious topics we can discuss. People are rightly nervous about it but I think it’s time.
9. The military, the police and the primary mechanisms of government law enforcement will be automated and smarter than humans. Make of this what you will.
10. Finally: the AI labs could end up nationalized in the strong sense. The American system doesn’t really seem compatible with this, to me, but there are many paths to nationalization that don’t seem off-limits in either a conservative or liberal political environment. It seems that in principle the labs can maintain coordination with the military and intelligence services on the backend without making an even bigger show of it than has already been made. The federal government having unilateral power of the kind we’re talking about is also extremely risky. Private companies having this power is different because they won’t generally speaking directly enact violence, and aren’t legally allowed to. I’m not a huge fan of nationalization but this world is confusing and apparently becoming more treacherous.
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*导出时间: 2026/7/6 22:10:24*
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## 中文翻译
# 关于近未来的46条思考
**作者**： bayes
**日期**： 2026-06-30T20:37:30.000Z
**来源**： [https://x.com/bayeslord/status/2072056960430789032](https://x.com/bayeslord/status/2072056960430789032)
---
这份清单基于我6月4日发布的一个帖子。这里做了少量修改和补充。有几个人希望我把那个帖子整理得更易读，于是就有了这篇文章。
## 智能水平
1. 我认为人们将被算法进步打个措手不及。整个世界——市场、政府、军队、公司、普通人等等——都在试图用近期的生产效率和规律、以及事情表面上看起来会如何发展的框架来理解AI及其影响。甚至几个自称“递归自我改进”（RSI）信仰者的新实验室，似乎也认为这将是老一套，只不过把Agent加进循环里而已。不是的。我的猜测是，智能生产的算法层面还有许多个数量级（OOM）的提升空间，也许（也许）多达十个，四到七个似乎更有可能。超过十个在原理上是可能的，但这与我所怀疑的宇宙真正允许我们做的程度相去甚远。难以置信但并非不可能。如果这是真的，那么事情实际上并不像它们看起来那样在发展，一个巨大的跳跃即将到来。任何这类情况的发生，都会让事情变得比几乎所有人目前定价所考虑的怪异得多。
2. 我们正处于起飞的早期。AI改进AI可能最终会成为历史上最具决定性的步骤之一。这并不确定，因为我们不知道自己距离智能的物理和计算极限有多远，不过我敢打赌距离还相当远（正如我在上面所说，单位规模下智能输出再提升约4-10个数量级似乎是可能的）。
3. 既然我们已经进入起飞阶段，算法研究正在加速。算力仍然是稀缺资源，但研究人员时间的机会成本降低了，因为你可以派一个Agent去执行任何探索或毫无希望的追寻。它可能会带回点什么。所有新想法都伴随着优化债务，而现在这些债务可以用无监督的token支出来偿还。大量研究中的规模法则曲线将被遍历。
4. AI模型，尤其是前沿模型，会持续变得更好。唯一真正的墙是物理学。模型正变得越来越自主、越来越聪明，而且一直在变好。数学和代码正在被“规模+强化学习”攻克，其他一切领域都排在后面。“可验证与不可验证”作为一个有意义的区分将逐渐消失。自动化AI研究和AI学习今后会显得越来越相关。训练好模型与模型一般性地学好东西密切相关。样本效率、创造力以及所有其他限制都将被解决，然后在任何规模上开始逼近算法最优。
5. 认为长程Agent总是需要同等长程训练的想法是错误的，因为时间上的泛化是存在的。长任务并不是由“长”构成的！这与LeCun的(1-e)^n误差累积谬论有关。真正发生的是误差校正。它发生在多个尺度上，从单个token的生成层面一直到长任务中的各个步骤。METR曲线之所以上升，部分原因是Agent开始达到误差校正的逃逸速度。
6. 一门工程级水平的深度学习科学即将到来。这将使我们以远超人们预期的速度迈向AI算法成熟，尽管正如我上面提到的，即使从原理上看也不清楚这能走多远。例如，一门尺度不变性的科学将极大地提升有用实验的规模和回报，因为在一块GPU上的实验可以告诉你如何使用十万块。
7. 人类技术活动的每一个领域都将迎来自己的“Move 37时刻”，然后相当快地，“Move 37”们就会显得古雅过时。我说的是所有领域。
8. 算力会持续改进。今天最好的矩阵乘法机器远未达到AI加速器的物理极限。数字硅还有很大的提升空间。新基板也有许多候选者，它们欠下的算法债务将被自动化地偿还到极限，但我们还不知道在空间/能源/时间/可制造性/成本上什么才是AI的最优基板。光子学和随机硅都是有趣的候选者，但我也预期奇点会是出人意料的。
9. 实验室能领先多远，部分取决于自动化和规模的回报，其中包括更深算法深度的回报。如果深度学习的实践（和理论）永远停留在浅层，那么从更长远的观点看，护城河将主要不是算法层面的，因为秘密将相对廉价地被发现。最终，蒸馏+数据+时间可以追上算力规模，尽管可能很慢。到目前为止，这似乎部分是我们所处的状况，但即使是真的，也不能保证会一直如此。
10. 如果随着规模扩大事情变得越来越不浅层，那么自动化和规模的每一点增量都会为你买到越来越让其他人望尘莫及的算法秘密。这似乎也部分是我们所处的状况。无论哪种情况，终点都是规模和研究的边际效用回报趋于饱和之时。我们不知道那在哪里。可能距今天还有2个数量级，也可能是20个数量级。没有人知道。
## 智能供应链
1. 至少在未来几年内，算力将是一种争夺激烈的资源。但在此之后，它将开始商品化，我们会嘲笑贫穷的2020年代。规模在不断扩大并取得成效，资本正在源源不断地涌入，让这个飞轮一次次转动。更多的矩阵乘法机器、更多的晶圆厂和更多的能源正在到来。智能生产的瓶颈只是暂时的——尽管潜在的经济减速因素依然存在。
2. 智能供应链的性质正在发生变化。目前它高度集中在各实验室周围。但实验室正在自动化的，恰恰是让它们强大的核心要素：研究人员和算法优势的发现。一旦这种情况开始发生，假设开源的追赶不至于太远，尤其是如果实验室不对AI研究员模型加以封锁，那么实验室的优势将来自更容易获得的资本、更多的算力、特殊的数据、商业关系和优秀的产品。当然，这取决于上述算法深度问题如何解决，以及其他一些因素。
3. 分布式训练将减少对单体式数据中心建设的需求，为非超大规模云厂商提供一些优势。不过，在纯粹的最大单体训练规模上，这仍无法超越超大规模云厂商。
4. 自动化AI实验将使算法秘诀得以广泛发现，因为这些秘诀天然比全规模训练更容易传播。不清楚这条路能走多远，但我预期会相当远。如前所述，深度学习的根本深度仍然未知，而这一点的上限也取决于此。
5. 有可能的是，尽管这些力量表面上有利于学术界和开源界，但由于算力的成本和机会成本，它们可能会陷入低迷。例如，GB300是用来运行GLM5.2更有价值，还是Fable更有价值？是在某个学术实验室做非前沿研究更有价值，还是在Anthropic内部构建Mythos 2更有价值？市场会自行解决需求最大的地方在哪里，而目前看来确实是各实验室。这意味着开源实验室可能会面临更严重的算力匮乏，*即使它们拥有资本*，如果它们没有提前锁定算力容量的话。即便锁定了，它们也会不断权衡自己做研究与出租算力之间的机会成本。参见Colossus与Anthropic之争。
6. 在AI能力开始变得愈发敏感的环境中（未来0-18个月内），开源在社会层面也可能开始遭遇困难，尤其是假设我们在加速安全工作方面依然迟缓——到目前为止我们确实如此。
7. 随着资本涌入各实验室，开源可能开始陷入低迷。这里存在一个协调问题：除了各实验室（或许还有政府），没人想要代币垄断，但如果这个问题能得到解决，且监管环境有利，也许一切都会顺利。
## 机器人技术
1. 机器人技术将迎来一个类似2022年11月ChatGPT式的时刻，随后还会有一个类似2025年11月Opus 4.5式的时刻。这两个时刻都尚未到来，但它们正在逼近，而且由于AI的快速进步（包括AI加速的物理系统工程），它们的到来会比人们想象的更快。看起来，机器人领域这两个时刻之间的间隔很可能不会是三年。
2. 然而，要在物理层面将世界上的机器人数量扩大，可能要到2030年或更晚。尽管我们每年生产约1亿辆汽车，而人形机器人比汽车小得多。考虑到我们每年还生产10亿部智能手机，如果资本和算法进展迅速，那么到2030年实现每年约1亿台机器人的产量似乎是合理的预期。每年1000万台肯定是可以实现的，因为无人机市场已经达到了这个规模。好的软件若能证明人形机器人在小规模下是值得的，就能吸引无限的资本，吸引力与证明的质量成正比。
3. 今天看来像是机器人技术硬限制的许多因素将会消失，例如样本效率低下、数据相对稀缺、昂贵或具有挑战性的手部和电机硬件设计、物理世界的分形复杂性，以及关于我们如何在世界上做事（比如管道工程）的隐藏且未被记录的知识。世界模型看起来很有用，但具体用什么并不重要。研究中的规模法则将被不断挖掘，直到边际效用递减。
4. 全球对机器人的需求轻松达到数百亿台，尤其是如果把各种形态都加总起来的话。有太多值得自动化的体力工作了。市场会努力解决这个问题，人们大概也不会去阻拦。
## 进展
1. 科学正在自动化和虚拟化。这意味着世界上许多我们所需要的进步将来自自动化实验室和模拟。我们尚不知道虚拟化的计算极限在哪里，但这类机器人驱动的生物、材料科学等领域的实验室将消除大量瓶颈，并在此过程中不断拓展经过验证的虚拟化的极限，以提高样本效率和实体化（reification）的净回报。基本上在每一个领域，我们都将把神经模型、显式模拟和真实世界实验以某种组合方式结合起来，共同提高生物学、材料科学等领域每美元和每单位时间的回报。
2. 进步法则无处不在。在深度学习领域，它们被称为缩放定律（scaling laws）。很难判断任何一条曲线的S型饱和何时出现，也很难判断地平线之外何时会出现新的S型曲线。这里需要理解的是，文明进步的引擎本身也有一条进步法则。我们的进步很可能像我们观察到的大多数自然过程一样属于饱和型，但我们实际上并不知道饱和会在哪里发生。技术和文明的成熟可能很近，也可能很远。我们（a）正处于这样一个历史阶段：我们几乎还没有为进步投入任何资源，但这种情况正在迅速改变；（b）我们正在让直接输出更多进步的机器实现自动化。我们的时代是有趣的时代。
3. 纵向扩展与横向扩展的未来。从0到1与从1到n。宇宙允许我们在广度和深度上取得多大的进步是一个开放性问题。广度比较容易估计，因为它类似于“从现在开始，物理定律总共允许我们进行多少步计算？”。而这种计算能达到多“深”（广义上的），则是未知的。在某些未来版本中，科技树足够深、可触及的计算宇宙足够丰富，我们将不断发明和发现，直到物理定律阻止我们（如果真的会被阻止的话）。另一些版本则较为平坦；我们很快就会耗尽一颗较浅的科技树，相对轻松地达到技术成熟，然后进行横向扩展，同样直到满足或物理定律阻止我们为止。
## 资本与生产
1. 更多的资本和更多的智能意味着强化的资本主义，这意味着我们更快地趋向市场均衡。随着时间的推移，这自然应当意味着大多数重要商品（包括人工智能、食品、住房、医疗、电子产品、娱乐和旅行）会出现通缩，以及向 epsilon 边际成本的竞争。这是建立在人们不会从中作梗的前提之上的。但在某些情况下，他们很可能会。
2. 采矿将被自动化。运输——陆运、海运和空运——将被自动化。工厂将被自动化。工厂工人将被自动化。配送中心将被自动化。整个供应链的维护、改进和扩张都将被自动化。
3. 人类在很长一段时间内仍将拥有工作。这将占人类的多大比例是一个开放性问题。那些声称这个比例会很高的人过于自信，那些声称这个比例会是零的人也同样如此。确实很难想象，在知识型工作的知识部分，人类还能在边际上贡献多久。某些事物的需求可能会大幅下降，比如医生——如果我们拥有每月20美元的超人类AI医生，外加按需检测，以及通过更好的医疗技术显著改善健康的话。然而，由于我们目前对医生实行了行业垄断式管制，我们可能会继续这样做，医生这个职业仍将是一个很好的职业。对娱乐的需求可能会增加，但制作成本会下降，而且娱乐业对人类的技术需求已经显著降低。然而，我们非常在乎其他人类，所以也许我们会继续在乎他们，演员这个职业将变得更有利可图。思考这一切将如何演变的一个方式，是考察今天的工人与消费者之间的供应链中有多少中间环节。对于TikTok网红来说，中间环节为零。对于医生来说，中间环节为零。对于工厂工人来说，则有很多环节。一项工作在多大程度上（a）可以被去中介化，或（b）可以在竞争中被打败，或（c）是可替代的，将在很大程度上决定其命运。这一分析相当微妙，这一段无法充分展开，但最后需要提及的是，这一切都假设不会出现需求端的骤然崩溃——如果太多的人不工作，而生产率/政府效率又不足以支撑全民基本收入（UBI）/全民基本收入保障（UHI），这种崩溃就有可能发生。
4. 与上述观点相关但并不矛盾的一点是：“永久底层阶级”可能成为现实。在较好的世界里，如果它确实存在，其形式可能更像是高度受限的自主性，而不是严重受限的收入。对大多数人来说，这最终会是没问题的——我们的自主性在现代社会中本就受到高度限制——但这需要心理上的适应，而适应可能需要时间，并且可能伴随着痛苦。
## 文化与心理学
1. 当前人类心理的成长与适应很缓慢，但这会改变。关键在于朝着好的方向改变，这对某些人来说可能并不容易。由于智能和自动化的大量涌现，我们将能够塑造出远比现在这套不适应环境、进化遗留的心理更稳健的心理。精神病学和心理学领域将在不过几十年的时间里经历相当于一千年的创新。人类将在根本上变得健康。粗糙、退化的“线头刺激”（wireheading）作为一种风险被高估了，因为我们将拥有远为精妙和多样的心智工程技术。
2. 在一个高度不确定的世界里，人们会比以往任何时候都更激烈地追逐权力、地位和财富，并在此过程中乐于背叛自己的同类。他们会发明各种理由来论证自己的行为是好的，甚至是伟大的。环顾四周吧。
3. 你会活着看到让你难以置信的尴尬场面。
4. 现在正上演着某种显而易见的双面话：那些有望成为或已经是最富有的 0.01% 的人说 AI 会惠及所有人，不用担心工作等等，但如果要他们在一年、五年或二十年内放弃自己的财富、成为地球上（甚至只是美国）一个随机普通人，他们又不肯。人们看得清这一点，并且已经开始有所反应。坦白说，我也不会放弃自己的位置，但我也没有说一切都会 perfectly fine（而且我也不是最富有的 0.01%）。其结果是我们有建成一个不公正世界的风险。有些人在乎这一点，我认为应该更经常地讨论它。还要完全说清楚的是，美国政治在应对这类关切方面的表现糟透了。
5. 马斯克似乎很可能成为第一位万亿（万亿美元级）富翁。广义上讲，不难想象对芯片、机器人和飞船的需求增长远超 1000 倍，而他很可能能捕获其中很大一部分。
## 协调
1. 在社会的各个层面上都需要更好的协调，这一点显而易见。就我们目前所理解的而言，更好的协调存在弱点和风险，但我们似乎还远未触及可能性的表面。会不会出现一个击败摩洛克（Moloch）的中本聪？
2. 至少在 AI 方面进行某种程度的国际协调很可能是件好事。我们可能需要条约和 GPU 清点。这种协调可以设计成（a）减缓敌对性的军事与政府权力积累的螺旋升级，以及（b）对科学和其他重要进步领域的影响降到最低。我们可能做不到这一点，因为 GPU 的用途太过广泛强大。核武器之所以能做到，是因为除了疯子之外没有人真的想使用核武器。
3. 由 AI 实验室协调一致地暂停或放慢 AI 研发，看起来比 2023 年更有可能发生。这其中有很多权衡，但我认为暂停的可辩护理由如今比 2023 年略强一些。当我们拥有了自动化研究之后，“暂停会被浪费掉”的论点就更难成立了，而我们现在还没有完全实现这一点（我们已经实现了自动化工程）。顺便说一句，我个人目前并不支持暂停，主要原因是它会破坏穿越奇点这条紧钢丝上的太多其他环节，科技树上可能有龙，而且对手是真实存在的。
## 权力、暴力、安全、自由
1. 我遗憾地告诉你，我们的宇宙在博斯特罗姆的意义上可能确实很脆弱。有可能，当前的世界存在一些我们无法足够快地协调控制的自由度，同时又要维持足以让我们世界得以成立的治理与自由规范——除了一种全景监狱式的体制之外。请注意，在这样的世界里，权力积累是一条滑坡。这些世界中的许多最终对大多数人来说可能会很糟糕。希望这不是真的，但它有可能是真的。
2. 无论存在何种潜在的限速因素，AI 扩散都会以大于零的速度发生。世界上有大量的计算机，而且算力与智能之间的兑换率正处于历史最低点。不要指望一切会停滞不前。
3. 永久底层阶级的想法意味着永久上层阶级的存在。这预设了某些人出于某种相对站不住脚的理由而拥有更多权利。其终极原因总是隐含或实现于以暴力为后盾的统治。但也许一个拥有先进 AI 的世界，是一个人类不再有正当治理权利的世界，任何人类都不再有公认的功绩或地位。这一点永远不会百分之百成立，但它可能变得越来越值得思考。我怀疑在道德层面和实际层面，两者的分歧在实践中会相当大，也许理应如此。
4. 制度将面临来自四面八方的转型压力，而这些力量可能导致暴政。通往那里的路径有很多，有的打着安全的幌子，有的通过良性的权力蔓延——其天花板是强大的 AI + 全自动军事供应链 + 全自动化武器。我们需要更好的制度。
5. 可能存在大量的零日漏洞。在网络、生物、基础设施、神经、模因学、物理学等领域。我们根本不了解这些领域中算法深度和一致性所带来的回报，无论是在防御和稳健性方面，还是在破坏方面。核弹的算法深度并没有超出世界上最聪明的人类的能力范围。明天，我们的机器将达到下一个台阶，再下一个。现在，我们对算法浅层性质的随机灾难发生率有一些了解，但对算法深层文明中会发生什么几乎一无所知。
6. 相关：科技树中可能存在一些非常糟糕的东西。我们真的不知道。
7. 大规模机器人能力在基于计算机的模型之外，带来了真正的夺权和政变式风险，以及更平凡的问题，比如新的网络攻击面和攻击向量。我们应该认真对待这些风险，并努力降低它们。
8. 相互确保毁灭建立在 20 世纪和 21 世纪初的技术之上。我们将在短时间内经历快速的技术变革，也许相当于一千年的进步量。这意味着 MAD 并非理所当然。这是可以解决的，但并不是完全确定或干净的颠覆，因为取得决定性优势的容错率非常低，甚至可能不可行。过去有些人以相当不严肃的方式提出过这个话题，我认为那是错误的，也是不负责任的。这是我们可以讨论的最严肃的话题之一。人们对它感到紧张是应当的，但我觉得是时候了。
9. 军队、警察以及政府执法的主要机制将被自动化，并且比人类更聪明。对此如何理解，由你决定。
10. 最后：AI 实验室可能最终会被强意义上的国有化。在我看来，美国的体制似乎与此并不真正兼容，但通往国有化的路径有很多，无论在保守派还是自由派的政治环境中，这些路径似乎都并非禁区。原则上，实验室似乎可以在后端与军方和情报机构保持协调，而不必比现在更加大张旗鼓。联邦政府拥有我们所说的这种单边权力，同样风险极大。私营公司拥有这种权力则不同，因为一般来说它们不会直接实施暴力，法律上也不允许。我不太喜欢国有化，但这个世界令人困惑，而且显然正变得更加险恶。
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