我的征尘是星辰大海。。。

The dirt and dust from my pilgrimage forms oceans of stars...

-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆

作者:黄教授

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大模型的第一增长曲线是存量知识的红利期

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原始脚本

大模型的第一增长曲线，存量知识红利的漫长时代。
近期 OpenAI Extra 项目传出动荡，一场发生在企业内部的路线争论。
恰好把整个 AI 行业最核心的底层矛盾摆到了台面上。
我们当下正在见证的模型能力爆发，究竟是通往未知心智的序章？还只是对人类已有文明成果的一次大规模再收割。
很多舆论喜欢用起点将至来解读每一次大模型的性能提升，却很少区分两种完全不同的能力跃迁。
超越人类个体和超越人类文明整体。
当前所有大模型所处的仅仅是第一条增长曲线，也就是存量知识红利期。
这条曲线还有极长的兑现周期，也是未来数年 AI 产业最确定的主线。
碳基生命自带一套无法突破的硬边界，哪怕是一个领域内最顶尖的天才。
能够用于深度学习、深度实践的有效时间仅有短短数十年。
人的精力、记忆力、单日可投入工作的时长、认知路径的惯性。
视野的边界全部存在物理上限。
即便是在同一个细分赛道当中，大量知识、经验、案例、碎片化的实践感悟散落在不同人的头脑。
零散文档、未成文的行业直觉里，永远不可能由单一的人类个体完整掌握。
以网络安全行业为例，海量潜在攻击链路、边缘设备的脆弱模式。
偏门漏洞的组合方式，至今仍存在大片无人系统性梳理的盲区。
这些盲区并不是理论上人类无法理解，而是没有任何一个人拥有足够的生命与精力。
把全部碎片信息串联归纳成完整的范式，这就是尚未采摘完毕的低垂果实。
大模型的核心优势恰恰就卡在这一处。
他并不天生拥有属于自己的原创认知，但是他拥有任何人类个体永远不可能具备的并行吞吐、大规模关联、跨样本归纳的能力。
它可以把分散在成千上万个体当中的隐性知识聚合起来，完成单个人永远完成不了的信息整合。
它可以做到比任何一位专家更快检索盲点。
拼接经验、推演组合，这种超越个体是确定、可落地、正在持续兑现的价值。
但我们必须清醒的划下一条边界。
超越人类个体不等于超越人类文明。
现阶段模型所有输出内容的素材全集依旧被人类已经产出过的信息牢牢包裹。
它能够做出人类从来没有想到过的组合，却很难生成从来没有被人类文明留下过痕迹的基础构建。
我们看到的新奇攻击思路、新颖的工程方案。
别具一格的理论推论，大多是已有零部件的全新排列，而非从零诞生的全新底层组件。
这条存量红利曲线并不会在某一个明确的时间节点突然宣告终结。
公开的、成文的资料会最先被消耗，之后挖掘的难度会持续抬升。
剩下的价值藏在只存在于人脑中的隐性经验，没有数字化的实践，只可意会难以用文字记录的行业直觉之中。
想要挖掘这一部分红利，瓶颈就不再是模型本身，而是一整套持续的知识数字化采集体系。
仅仅依靠这条增长曲线，就足以支撑 AI 产业长达数十年的迭代与商业产出。
这是一条非常漫长的收获期，而那条真正通往未知的第二曲线，让大模型独立开辟人类从未触及的认知赛道，提出人类从未设想过的问题。
建立意志的认知框架，甚至完成自主的物质实践闭环，距离我们还太过遥远。
我们当下甚至没有一套成熟的话语体系，用来严谨讨论这件事发生的可能性。
发生的路径以及它带来的全部后果。
现在就投入大量资源去赌这条模糊的道路，对于商业主体而言，是一场回报周期完全不可预测的豪赌。
这正是 Extra 风波背后的内在冲突。
OpenAI 内部的撕裂，本质上就是两条路线的博弈。
一边是优先深耕存量红利。
快速兑现商业化价值，走一条路径清晰、收益稳定的路。
另一边则希望跳出现有范式，去冲击那条看不见终点的第二曲线。
资本市场舆论大多热衷于炒作遥远的起点叙事，但是产业现实依然牢牢停留在第一阶段。
未来很长一段时间里，AI发展的主线依旧是持续榨取散落在人类文明各处，还没有被整合完毕的存量知识。
我们会不断看见模型一次次刷新对于单个领域专家的优势，不断解决过去因为人类个体经历局限而搁置的问题。
至于能不能跨过认知边界，闯入人类从未踏足的知识荒原？那是下一个时代的命题，不在我们当下的讨论范围之内。
认清第一阶段红利的本质与漫长周期，才是理解当前 AI 发展最核心的钥匙。

修正脚本

大模型的第一增长曲线，存量知识红利的漫长时代。
近期 OpenAI Extra 项目传出动荡，一场发生在企业内部的路线争论。
恰好把整个 AI 行业最核心的底层矛盾摆到了台面上。
我们当下正在见证的模型能力爆发，究竟是通往未知心智的序章？还只是对人类已有文明成果的一次大规模再收割。
很多舆论喜欢用起点将至来解读每一次大模型的性能提升，却很少区分两种完全不同的能力跃迁。
超越人类个体和超越人类文明整体。
当前所有大模型所处的仅仅是第一条增长曲线，也就是存量知识红利期。
这条曲线还有极长的兑现周期，也是未来数年 AI 产业最确定的主线。
碳基生命自带一套无法突破的硬边界，哪怕是一个领域内最顶尖的天才。
能够用于深度学习、深度实践的有效时间仅有短短数十年。
人的精力、记忆力、单日可投入工作的时长、认知路径的惯性。
视野的边界全部存在物理上限。
即便是在同一个细分赛道当中，大量知识、经验、案例、碎片化的实践感悟散落在不同人的头脑。
零散文档、未成文的行业直觉里，永远不可能由单一的人类个体完整掌握。
以网络安全行业为例，海量潜在攻击链路、边缘设备的脆弱模式。
偏门漏洞的组合方式，至今仍存在大片无人系统性梳理的盲区。
这些盲区并不是理论上人类无法理解，而是没有任何一个人拥有足够的生命与精力。
把全部碎片信息串联归纳成完整的范式，这就是尚未采摘完毕的低垂果实。
大模型的核心优势恰恰就卡在这一处。
它并不天生拥有属于自己的原创认知，但是它拥有任何人类个体永远不可能具备的并行吞吐、大规模关联、跨样本归纳的能力。
它可以把分散在成千上万个体当中的隐性知识聚合起来，完成单个人永远完成不了的信息整合。
它可以做到比任何一位专家更快检索盲点。
拼接经验、推演组合，这种超越个体是确定、可落地、正在持续兑现的价值。
但我们必须清醒地划下一条边界。
超越人类个体不等于超越人类文明。
现阶段模型所有输出内容的素材全集依旧被人类已经产出过的信息牢牢包裹。
它能够做出人类从来没有想到过的组合，却很难生成从来没有被人类文明留下过痕迹的基础构建。
我们看到的新奇攻击思路、新颖的工程方案。
别具一格的理论推论，大多是已有零部件的全新排列，而非从零诞生的全新底层组件。
这条存量红利曲线并不会在某一个明确的时间节点突然宣告终结。
公开的、成文的资料会最先被消耗，之后挖掘的难度会持续抬升。
剩下的价值藏在只存在于人脑中的隐性经验，没有数字化的实践，只可意会难以用文字记录的行业直觉之中。
想要挖掘这一部分红利，瓶颈就不再是模型本身，而是一整套持续的知识数字化采集体系。
仅仅依靠这条增长曲线，就足以支撑 AI 产业长达数十年的迭代与商业产出。
这是一条非常漫长的收获期，而那条真正通往未知的第二曲线，让大模型独立开辟人类从未触及的认知赛道，提出人类从未设想过的问题。
建立自主的认知框架，甚至完成自主的物质实践闭环，距离我们还太过遥远。
我们当下甚至没有一套成熟的话语体系，用来严谨讨论这件事发生的可能性。
发生的路径以及它带来的全部后果。
现在就投入大量资源去赌这条模糊的道路，对于商业主体而言，是一场回报周期完全不可预测的豪赌。
这正是 Extra 风波背后的内在冲突。
OpenAI 内部的撕裂，本质上就是两条路线的博弈。
一边是优先深耕存量红利。
快速兑现商业化价值，走一条路径清晰、收益稳定的路。
另一边则希望跳出现有范式，去冲击那条看不见终点的第二曲线。
资本市场舆论大多热衷于炒作遥远的起点叙事，但是产业现实依然牢牢停留在第一阶段。
未来很长一段时间里，AI发展的主线依旧是持续榨取散落在人类文明各处，还没有被整合完毕的存量知识。
我们会不断看见模型一次次刷新对于单个领域专家的优势，不断解决过去因为人类个体经历局限而搁置的问题。
至于能不能跨过认知边界，闯入人类从未踏足的知识荒原？那是下一个时代的命题，不在我们当下的讨论范围之内。
认清第一阶段红利的本质与漫长周期，才是理解当前 AI 发展最核心的钥匙。

英文翻译

The first growth curve of large models is the long era of stock knowledge dividend.
Recently, turbulence has been reported on the OpenAI Extra project, which is a route dispute within the enterprise.
It just puts the core underlying contradiction of the entire AI industry on the table.
Is the explosion of model capabilities we are witnessing right now a prologue to an unknown mind, or just a large-scale re-harvesting of the existing achievements of human civilization?
Many public opinions like to interpret every performance improvement of large models with the narrative that the starting point is coming, but rarely distinguish between two completely different capability leaps.
Surpassing individual human beings, and surpassing human civilization as a whole.
All current large models are only in the first growth curve, that is, the stock knowledge dividend period.
This curve still has an extremely long realization cycle, and it is also the most definite main line of the AI industry in the next few years.
Carbon-based life inherently has a set of unbreakable hard boundaries, even for the top genius in a field.
The effective time available for in-depth learning and in-depth practice is only a few decades.
Human's energy, memory, daily working hours, inertia of cognitive path,
and the boundary of vision all have physical upper limits.
Even in the same segmented track, a large amount of knowledge, experience, cases and fragmented practical insights are scattered in the minds of different people,
scattered documents and unwritten industry intuition, which can never be fully mastered by a single human individual.
Take the cybersecurity industry as an example: massive potential attack links, vulnerability modes of edge devices,
and the combination of niche vulnerabilities still have large blind spots that no one has systematically sorted out so far.
These blind spots are not theoretically incomprehensible to humans, but no one has enough life and energy
to connect all fragmented information and summarize it into a complete paradigm. These are the low-hanging fruits that have not been fully picked yet.
The core advantage of large models lies exactly here.
It does not inherently have its own original cognition, but it has the capabilities of parallel throughput, large-scale association, and cross-sample induction that no individual human can ever have.
It can aggregate the tacit knowledge scattered among thousands of individuals, and complete the information integration that a single person can never accomplish.
It can retrieve blind spots faster than any expert.
Splicing experience and deducing combinations, this kind of transcendence over individuals is definite, implementable, and is continuously delivering value.
But we must clearly draw a boundary.
Surpassing individual humans does not equal surpassing human civilization.
At the current stage, the complete set of materials for all output content of the model is still firmly wrapped by the information that humans have already produced.
It can make combinations that humans have never thought of, but it is difficult to generate basic constructs that have never been left any trace by human civilization.
The novel attack ideas and new engineering solutions we see,
and distinctive theoretical inferences are mostly new arrangements of existing parts, rather than brand-new underlying components born from scratch.
This stock dividend curve will not suddenly declare its end at a certain clear time node.
Public, written documents will be consumed first, and then the difficulty of mining will continue to rise.
The remaining value is hidden in the tacit experience that only exists in human brains, un-digitized practices, and industry intuition that can only be sensed but is difficult to record in words.
To tap this part of the dividend, the bottleneck is no longer the model itself, but a complete set of continuous digital knowledge collection system.
Relying on this growth curve alone is enough to support decades of iteration and commercial output of the AI industry.
This is a very long harvest period. As for the real second curve leading to the unknown, which allows large models to independently open up cognitive tracks that humans have never touched, raise questions that humans have never conceived,
establish independent cognitive frameworks, and even complete a closed loop of independent material practice, it is still too far away from us.
At present, we do not even have a mature discourse system to rigorously discuss the possibility of this happening,
the path of its occurrence and all the consequences it brings.
Investing a lot of resources to bet on this vague path now is a big gamble with a completely unpredictable return cycle for commercial entities.
This is exactly the internal conflict behind the Extra turmoil.
The split within OpenAI is essentially a game between two routes.
One side prioritizes deep cultivation of stock dividends,
quickly realizes commercial value, and takes a path with clear direction and stable returns.
The other side hopes to jump out of the existing paradigm and impact the second curve with no visible end.
Most public opinions in the capital market are keen to hype the narrative of a distant starting point, but the industrial reality still firmly stays in the first stage.
For a long time to come, the main line of AI development will still be to continuously extract the stock knowledge scattered all over human civilization that has not been fully integrated.
We will continue to see models repeatedly refresh their advantages over individual domain experts, and continuously solve problems that were put on hold in the past due to the limited experience of individual humans.
As for whether we can cross the cognitive boundary and break into the knowledge wilderness that humans have never set foot on? That is the proposition of the next era, and it is not within the scope of our current discussion.
Recognizing the essence and long cycle of the dividend in the first stage is the core key to understanding the current development of AI.