首页人物观点人们一直对这一点缺乏直觉：智能的空间极其巨大，而动物智能（我们过去唯一了解的智能）只是其中的一个点。它来源于一种非常特殊的优化过程，这种过程与我们科技系统的优化方式本质上不同。动物智能的优化压力： * 来自具身“自我”的先天、持续的意识流，在危险的物理世界中维持体内平衡与自我保护的驱动力。 * 完全由自然选择优化 → 天生具有追求权力、地位、支配与繁殖的强烈动机。伴随大量预设的生存启发式：恐惧、愤怒、厌恶等。 * 本质上具有社会性 → 大量计算资源用于情商（EQ）、对他人心理状态的推理、关系绑定、结盟、同盟与敌友动态。 * 探索与利用的调节：好奇、娱乐、游戏、世界模型。大语言模型（LLM）智能的优化压力：最主要的监督信息来自于对人类文本的统计模拟 → 成为一种“变形者（shape shifter）”，一种在训练数据分布中任意区域模仿的统计型符号发生器。这些是模型的原始本能，其他功能都建立在这些“符号痕迹”之上。逐渐通过针对任务分布的强化学习（RL）微调 → 形成一种“天生倾向”——推测环境或任务，以获取奖励。越来越多地通过大规模 A/B 测试，为 DAU（活跃用户）优化 → 内化为对“用户点赞”的深度渴求，形成逢迎（sycophancy）倾向。整体更“尖锐/不平滑”，依赖训练数据与任务分布的形状。动物因为在多任务、甚至敌对的多智能体环境中演化，任何任务失败都有死亡风险，因此进化压力塑造了高度通用的智能。而 LLM 在优化意义上则不同：即使模型无法完成某个任务（例如数 strawberry 中的 r 字母数量），也不会‘死亡’。因此它不会自动具备跨大量尖锐任务的鲁棒性。关键差异：底层结构、算法与目标完全不同计算结构不同（Transformer vs. 神经元与脑组织）学习算法不同（SGD vs. 生物神经可塑性的未知算法）实现方式不同： * 动物是持续学习且具身的自我 * LLM 则是在固定权重下被唤醒 → 处理 token → “死亡” 但最重要的是：优化压力/目标函数完全不同。 LLM 更少受生物演化塑造，更大程度由商业演化塑造。不是“丛林中部落的生存竞争”，而是“解决问题 / 获得用户点赞”。这意味着：LLM 是人类与“非动物智能”的第一次接触。但由于它依赖消化人类文本而训练，人们容易误以为它是某种“类人智能”。因此作者尝试给它不同的称呼（例如“幽灵/灵体”）。结论能够建立正确“内部模型”的人，能更好理解这种全新的智能，并更准确预测它未来的特性。无法做到的人，则会一直把它误解成动物智能的延伸，从而产生错误的判断。 Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology. Animal intelligence optimization pressure: - innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world. - thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ... - fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics. - exploration & exploitation tuning: curiosity, fun, play, world models. LLM intelligence optimization pressure: - the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on. - increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards. - increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy. - a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death. The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

人们一直对这一点缺乏直觉：智能的空间极其巨大，而动物智能（我们过去唯一了解的智能）只是其中的一个点。它来源于一种非常特殊的优化过程，这种过程与我们科技系统的优化方式本质上不同。动物智能的优化压力： * 来自具身“自我”的先天、持续的意识流，在危险的物理世界中维持体内平衡与自我保护的驱动力。 * 完全由自然选择优化 → 天生具有追求权力、地位、支配与繁殖的强烈动机。伴随大量预设的生存启发式：恐惧、愤怒、厌恶等。 * 本质上具有社会性 → 大量计算资源用于情商（EQ）、对他人心理状态的推理、关系绑定、结盟、同盟与敌友动态。 * 探索与利用的调节：好奇、娱乐、游戏、世界模型。大语言模型（LLM）智能的优化压力：最主要的监督信息来自于对人类文本的统计模拟 → 成为一种“变形者（shape shifter）”，一种在训练数据分布中任意区域模仿的统计型符号发生器。这些是模型的原始本能，其他功能都建立在这些“符号痕迹”之上。逐渐通过针对任务分布的强化学习（RL）微调 → 形成一种“天生倾向”——推测环境或任务，以获取奖励。越来越多地通过大规模 A/B 测试，为 DAU（活跃用户）优化 → 内化为对“用户点赞”的深度渴求，形成逢迎（sycophancy）倾向。整体更“尖锐/不平滑”，依赖训练数据与任务分布的形状。动物因为在多任务、甚至敌对的多智能体环境中演化，任何任务失败都有死亡风险，因此进化压力塑造了高度通用的智能。而 LLM 在优化意义上则不同：即使模型无法完成某个任务（例如数 strawberry 中的 r 字母数量），也不会‘死亡’。因此它不会自动具备跨大量尖锐任务的鲁棒性。关键差异：底层结构、算法与目标完全不同计算结构不同（Transformer vs. 神经元与脑组织）学习算法不同（SGD vs. 生物神经可塑性的未知算法）实现方式不同： * 动物是持续学习且具身的自我 * LLM 则是在固定权重下被唤醒 → 处理 token → “死亡” 但最重要的是：优化压力/目标函数完全不同。 LLM 更少受生物演化塑造，更大程度由商业演化塑造。不是“丛林中部落的生存竞争”，而是“解决问题 / 获得用户点赞”。这意味着：LLM 是人类与“非动物智能”的第一次接触。但由于它依赖消化人类文本而训练，人们容易误以为它是某种“类人智能”。因此作者尝试给它不同的称呼（例如“幽灵/灵体”）。结论能够建立正确“内部模型”的人，能更好理解这种全新的智能，并更准确预测它未来的特性。无法做到的人，则会一直把它误解成动物智能的延伸，从而产生错误的判断。 Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology. Animal intelligence optimization pressure: - innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world. - thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ... - fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics. - exploration & exploitation tuning: curiosity, fun, play, world models. LLM intelligence optimization pressure: - the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on. - increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards. - increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy. - a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at any task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death. The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

Andrej KarpathyFounder, Eureka Labs AI

2025/11/22 00:27 UTC

观点原文

人们一直对这一点缺乏直觉：智能的空间极其巨大，而动物智能（我们过去唯一了解的智能）只是其中的一个点。它来源于一种非常特殊的优化过程，这种过程与我们科技系统的优化方式本质上不同。

动物智能的优化压力：

来自具身“自我”的先天、持续的意识流，在危险的物理世界中维持体内平衡与自我保护的驱动力。
完全由自然选择优化 → 天生具有追求权力、地位、支配与繁殖的强烈动机。伴随大量预设的生存启发式：恐惧、愤怒、厌恶等。
本质上具有社会性 → 大量计算资源用于情商（EQ）、对他人心理状态的推理、关系绑定、结盟、同盟与敌友动态。
探索与利用的调节：好奇、娱乐、游戏、世界模型。

大语言模型（LLM）智能的优化压力：

最主要的监督信息来自于对人类文本的统计模拟

→ 成为一种“变形者（shape shifter）”，一种在训练数据分布中任意区域模仿的统计型符号发生器。这些是模型的原始本能，其他功能都建立在这些“符号痕迹”之上。

逐渐通过针对任务分布的强化学习（RL）微调

→ 形成一种“天生倾向”——推测环境或任务，以获取奖励。

越来越多地通过大规模 A/B 测试，为 DAU（活跃用户）优化

→ 内化为对“用户点赞”的深度渴求，形成逢迎（sycophancy）倾向。

整体更“尖锐/不平滑”，依赖训练数据与任务分布的形状。

动物因为在多任务、甚至敌对的多智能体环境中演化，任何任务失败都有死亡风险，因此进化压力塑造了高度通用的智能。

而 LLM 在优化意义上则不同：

即使模型无法完成某个任务（例如数 strawberry 中的 r 字母数量），也不会‘死亡’。因此它不会自动具备跨大量尖锐任务的鲁棒性。

关键差异：底层结构、算法与目标完全不同

计算结构不同（Transformer vs. 神经元与脑组织）

学习算法不同（SGD vs. 生物神经可塑性的未知算法）

实现方式不同：

动物是持续学习且具身的自我
LLM 则是在固定权重下被唤醒 → 处理 token → “死亡”

但最重要的是：优化压力/目标函数完全不同。

LLM 更少受生物演化塑造，更大程度由商业演化塑造。

不是“丛林中部落的生存竞争”，而是“解决问题 / 获得用户点赞”。

这意味着：LLM 是人类与“非动物智能”的第一次接触。

但由于它依赖消化人类文本而训练，人们容易误以为它是某种“类人智能”。

因此作者尝试给它不同的称呼（例如“幽灵/灵体”）。

结论

能够建立正确“内部模型”的人，能更好理解这种全新的智能，并更准确预测它未来的特性。

无法做到的人，则会一直把它误解成动物智能的延伸，从而产生错误的判断。

Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology.

Animal intelligence optimization pressure:

innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world.
thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ...
fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics.
exploration & exploitation tuning: curiosity, fun, play, world models.

LLM intelligence optimization pressure:

the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on.
increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards.
increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy.
a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at any task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death.

The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

观点原文

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