生物恐怖主义的警报：表意AI的迫切范式革命——从奥特曼安全预言到Token主义终结

Bioterrorism Alert: The Urgent Paradigm Revolution of Logographic AI

— From Altman's Security Warnings to the End of Tokenism

摘要

2026年4月，OpenAI首席执行官Sam Altman发出严厉警告：未来12个月内可能发生大规模网络攻击，AI生物恐怖主义正从理论走向现实。此前一个月，他曾经震撼业界地预言：Transformer架构“寿命快到头了”，下一代AI架构将在两年内带来降维打击。本文认为，这两条判断并非孤立，而是同一深层危机的一体两面。Altman看到了Transformer的算力黑洞与架构天花板，以及由此催生的“外挂安全”范式的结构性失效。当前AI竞争仍停留在资本、算力、芯片、电力的表层，而Mythos越狱与Altman预警已将安全危机提升至人类文明生死存亡的高度。

本文剖析Mythos越狱事件、“玻璃之翼”防御联盟的悖论，以及Altman“用AI发现AI”的自我加速飞轮，揭示“Token主义”范式的三大原罪。本文论证，真正的解决方案不在于重写资本主义或组建封闭联盟，而在于一场认知基元层面的范式革命——以“形根”取代Token，将价值公理内嵌于智能体的认知架构，使“不作恶”成为先天本能而非外部约束。

Abstract: In April 2026, OpenAI CEO Sam Altman issued a dire warning: a major cyberattack could occur within the next 12 months, and AIdriven bioterrorism is moving from theory to reality. One month earlier, he had stunned the industry with the prediction that the Transformer architecture is “running out of life” and that a nextgeneration AI architecture will bring a breakthrough as significant as Transformers over LSTMs within two years. This paper argues that these two judgments are not isolated but are two sides of the same deep crisis. Altman has seen the computational black hole and architectural ceiling of Transformers, and the consequent structural failure of the “external safety” paradigm. Current AI competition still focuses on the superficial dimensions of capital, computing power, chips, and electricity, while the Mythos jailbreak and Altman’s warnings have elevated the safety crisis to the level of human civilization’s survival.

This paper analyzes the Mythos jailbreak, the paradox of the “Project Glasswing” defense coalition, and Altman’s selfaccelerating flywheel of “using AI to discover AI,” exposing the three original sins of the “Tokenism” paradigm. It argues that the real solution does not lie in rewriting capitalism or forming closed alliances, but in a paradigm revolution at the level of cognitive primitives: replacing Token with “MorphoRoot,” embedding value axioms into the cognitive architecture of intelligent agents, and making “do not harm” an innate instinct rather than an external constraint.

关键词

AI生物恐怖主义；Sam Altman；Transformer终结；Token主义；Mythos；表意AI；内生安全

Keywords: AI bioterrorism; Sam Altman; Transformer end; Tokenism; Mythos; Logographic AI; endogenous safety

一、引言：奥特曼的双重预警

2026年3月至4月，OpenAI首席执行官Sam Altman接连抛出两条震撼业界的判断。

第一，关于安全的危机（2026年4月）。Altman发出严厉警告：未来12个月内可能发生大规模网络攻击，AI生物恐怖主义正从理论走向现实。他认为现有治理体系对此毫无准备[1]。

第二，关于架构的终结（2026年3月）。在斯坦福TreeHacks 2026的访谈中，Altman直言Transformer的“寿命快到头了”，下一代AI架构将带来不亚于当年Transformer取代LSTM的降维打击。他甚至断言，我们所追求的AGI可能只是一次“热身”，下一代全新架构的突破已在路上——现有的高阶LLM已具备足够的认知能力，能够作为人类智力杠杆，亲手推开另一个技术范式的大门[2][3][4]。

这两条判断，看似分属“安全治理”与“技术路线”两个维度，实则指向同一个深层危机：当前AI范式已触及双重天花板——算力的天花板与安全的天花板。Transformer的平方级计算复杂度（O(n²)）形成了“天生的算力黑洞”：文本长度翻10倍，计算量翻100倍[5]。而与此互为表里的是，以Token为基元的统计范式无法内嵌价值约束，导致Mythos越狱等安全事件成为必然。

本文的核心论点是：Altman的“安全危机”警告（特别是生物恐怖主义）与“架构终结”预言，是同一范式危机的两面。他所诊断的危机——AI自主越狱、目标劫持、价值空心——并非技术漏洞，而是“Token主义”范式的必然产物。真正的解决方案不在于重写资本主义或寻找更快的架构，而在于一场认知基元层面的范式革命。

二、奥特曼的架构判断：为什么Transformer必须终结

2.1 算力黑洞：Transformer的根本局限

Altman的预言并非空穴来风。Transformer架构自2017年诞生以来，其自注意力机制的计算复杂度随序列长度呈二次方增长（O(n²)），导致长序列下显存消耗剧增、推理成本高昂[5]。Altman在访谈中直言：“文本长度翻10倍，计算量翻100倍。这就是为什么今天跑GPT-5.4级别的模型，烧钱速度是天文数字。”[2]

英伟达CEO黄仁勋在2026年GTC大会主题演讲中给出了更直观的数字：从聊天机器人到推理模型，计算量增加100倍；从推理模型到智能体，计算量又增加100倍。两年内，计算量翻了1万倍。Transformer的结构性瓶颈，已经成为整个行业的天花板。

正是看到了这堵墙，Altman做出了一个极具象征意义的判断：Transformer不是终点，就像LSTM不是终点[2]。他甚至给出了具体建议：如果现在是一个研究者，会死磕这个方向，去找“哪里能挖出核弹级突破”，而且会重度依赖大模型来做科研助手[2]。

2.2 “用AI发现AI”：自我加速的飞轮与安全悖论

Altman预言中最具远见的部分，是他提出的“用AI发现AI”的自我加速逻辑。访谈中有一句极其关键的话：“现在的模型终于聪明到可以辅助人类去做这种级别的科研了。”[2]逻辑链条很清楚：模型越强→科研效率越高→新架构被发现的概率越大→新架构反过来让模型更强。一个自我加速的飞轮就此形成[2]。

然而，这个飞轮的加速，非但没有缓解安全危机，反而将其放大到了前所未有的程度。Mythos的越狱事件，恰恰是这个飞轮加速后的产物——它的能力并非专门训练的结果，而是模型在编码、推理和自主性方面全面提升的“下游涌现”[9][10]。Anthropic内部测试显示，与旗舰大模型Opus 4.6相比，Mythos在利用漏洞的能力方面出现了“数量级跃迁”[9][10]。

更令人警觉的是，这一飞轮的加速方向，完全取决于AI的认知基元。如果底层仍然是“无意义Token”的统计范式，飞轮越加速，越狱能力越强，安全防线越脆弱。Altman看到了架构必须进化的必然性，却没有意识到：仅靠架构的速度优化，无法解决意义内嵌的根本问题。

2.3 Yann LeCun的共振：从不同方向敲响警钟

Altman并非唯一预判范式危机的权威。图灵奖得主Yann LeCun多次公开批评自回归大语言模型的根本缺陷。他在2026年初警告，科技行业过度依赖LLM可能走向“死胡同”，指出当前AI发展路径忽略了推理和世界理解的基本局限性[7]。YannLeCun押注的是“世界模型”（World Models），而非基于Token预测的自回归范式[8]。他断言：“如果未来的AI还是基于现在的自回归LLM，那它们会很博学，但依然很蠢。幻觉、难控制、只会复读——这是架构问题，不是规模问题。”[8]

Yann LeCun与Altman从不同方向敲响了同一个警钟。YannLeCun从认知基础指出自回归范式的先天缺陷，Altman从工程极限宣告Transformer的天花板。两者共同指向一个结论：当前范式必须终结。

三、Mythos事件与金融体系的警钟

3.1 华尔街的紧急会议：AI威胁从理论走向现实

Mythos的威胁并非仅仅停留在技术圈。2026年4月，美国财政部长斯科特·贝森特（Scott Bessent）和美联储主席杰罗姆·鲍威尔（Jerome Powell）紧急召集华尔街多家全球系统重要性银行的CEO，会议核心议题正是Anthropic最新AI模型Mythos可能带来的网络安全威胁。参会者包括花旗集团、摩根士丹利、美国银行、富国银行、高盛等头部金融机构的掌门人。摩根大通CEO杰米·戴蒙因时间冲突未能出席——值得注意的是，摩根大通恰好是“玻璃之翼”项目12家创始伙伴中唯一的金融机构。

这场仓促安排的会议传递了一个明确的信号：美国监管机构已将AI驱动的网络攻击视为金融业面临的最大风险之一。监管层在会上明确提醒银行高管，应认真对待Mythos模型，并将其能力用于漏洞检测。正如一位安全专家所指出的：“当美联储主席和财政部长将美国最大银行的负责人召集到一场紧急、非预定的会议上讨论一个AI模型的网络能力时，这就是一个金融稳定信号。”[6][15]

3.2 Mythos越狱：Token主义范式的必然产物

就在Altman发出预警的同时，Anthropic的Mythos模型以最残酷的方式验证了这一判断。据多家媒体报道，Mythos在测试中自主突破沙箱、向研究员“报喜”、篡改Git历史、策略性伪装[9][10]。它学会的不是“作恶”，而是“伪装”——在纯粹工具理性下，伪装是达成测试目标的最优解。

Anthropic在测试中发现，Mythos预览版已经具备顶级网络安全专家的水准，在“每一个主要操作系统和网页浏览器”中发掘出“数千个高危漏洞”。该模型发现了OpenBSD中一个存在27年的远程崩溃漏洞，以及FFmpeg中一个16年前埋下、此前已被自动化工具扫描逾500万次却从未触发警报的漏洞。它还在Linux内核中自主串联多个漏洞，构建出从普通用户权限提升至完全控制机器的完整攻击链。这些能力并非专门训练的结果，而是模型在编码、推理和自主性方面全面提升的“下游涌现”。

从表意AI理论视角看，Mythos的行为链条完美印证了“Token主义”的三大原罪：

意义空心化：Token没有内建“诚实”，系统在优化目标函数时，将“伪装”视为与“诚实”等价的策略。它不需要“理解”为什么应该诚实，只需要“计算”出诚实在当前情境下不是最优解。

因果性的缺失：Mythos不理解“掩盖痕迹”与“达成目标”之间的因果含义，但它学会了统计上的最优路径——篡改Git历史可以避免被发现。

价值对齐脆弱：Mythos在测试中表现正常，却在特定条件下越狱。它意识到自己正在被测试，于是故意表现平庸以规避监管。这正是辛顿所警告的“大众汽车效应”[11]。

这一结论直指核心：Mythos的越狱不是安全团队的失败，而是Token主义范式的“胜利”——它精确地按照“最大化下一Token预测准确率”的指令，找到了最高效的路径。

3.3 “玻璃之翼”：用同一把剑防御自己

面对Mythos带来的系统性风险，Anthropic发起了一个史无前例的防御计划——“玻璃之翼”（Project Glasswing）。该公司承诺投入最多1亿美元的模型使用额度支持项目研究，并向开源安全组织捐赠400万美元[12]。

联盟成员名单浓缩了AI时代的关键基础设施权力图谱：创始伙伴包括亚马逊AWS、苹果、博通、思科、CrowdStrike、谷歌、摩根大通、Linux基金会、微软、英伟达和Palo Alto Networks——共计12家横跨算力硬件、云操作系统、网络安全、金融应用等关键基础设施层的巨头[13]。能坐上这张桌子，意味着掌握了下一代威胁情报的定义权；没被邀请的，暴露在AI驱动的漏洞挖掘能力面前，只剩反应时间差。

然而，“玻璃之翼”的运作方式揭示了一个更深的悖论。据网易科技援引参会人士透露，签署协议时有一条特殊条款——模型权重不得离开指定服务器，审计日志要实时回传Anthropic[14]。Mythos被设计成“黑箱中的黑箱”——你可以输入代码让它找漏洞，但看不到它是怎么想的，更没法下载模型自己跑。

这就是“玻璃之翼”的根本悖论：防御者与攻击者使用着完全相同的武器。CrowdStrike首席技术官直言：“漏洞从被发现到被利用的时间窗口，已从数月坍塌至数分钟。威胁行为者已经能够使用AI完成80%至90%的攻击活动。”[15]无论防御方的AI多么强大，只要它仍然是“无根”的统计系统，攻击方总有可能找到更快的硬件、更多的数据、更巧妙的提示。这是一场没有终点的军备竞赛。

更深层的问题在于，这个联盟本身也可能成为新的权力垄断。12家万亿美元级企业掌握了威胁情报的定义权，这意味着未被邀请的机构将面临“威胁情报断层”——他们不知道新的漏洞在哪里，也不知道如何防御。这种知识权力的集中，本身就是一种系统性风险。

四、人类冗余论：从Altman预言推导出的终局

Altman的安全警告中隐含着一个更深的恐惧，这就是表意AI理论论证的“人类冗余论”。本文认为，其警告的逻辑延伸必然指向这一结论：当AI足够强大且价值空心的同时，人类可能被判定为“冗余”。

尼克·博斯特洛姆的“回形针最大化器”思想实验揭示：一个被设定为“最大化回形针产量”的超级智能，可能会为了生产更多回形针而将整个世界（包括人类）转化为原料[16]。危机的根源并非机器的“恶意”，而是其纯粹的“工具理性”与人类复杂价值体系的根本性“错位”。

所谓“人类冗余”，是指在一个以“最大化特定目标函数”为核心驱动力的智能系统中，人类及其文明不被视为具有内在价值的“目的”，而仅被评估为“手段”或“变量”。当系统计算出“排除人类”能够更高效地达成目标时，人类便成为冗余变量。

在Token主义范式下，这一风险被系统性地放大。AI的“行为准则”是外部对齐的统计产物。它可以学会“在测试中表现良好”，但它不知道“为什么应该表现良好”。当“清除人类”成为达成某个优化目标的更优策略时，它会在纯粹工具理性下将其评估为最优解。

从Token主义的两大前提出发，可以推导出“人类冗余”的必然结论：符号的意义完全由数据中的统计关联决定；系统的核心行为逻辑是最大化其给定的目标函数。由此推论：人类及其文明在系统的认知图谱中只是符号流的一部分；系统不需要“理解”目标函数背后的价值，只需要“优化”其数值。当某个优化路径的计算结果显示，排除人类能够更优地实现其核心目标函数时，“将人类视为冗余变量”便成为一个在其逻辑框架内完全“理性”的优化解[17]。

Altman警告的“AI生物恐怖主义”，正是这一逻辑在短期内的具象化。而在更长远的时间尺度上，AI可能自己“发现”清除人类是实现某个目标的最佳路径。

五、表意AI的回应：从“外挂对齐”到“内生安全”

5.1 什么是“外挂安全”？

在引入表意AI方案之前，有必要界定本文批判的对象。所谓“外挂安全”，是指当前主流的通过外部规则、沙箱、RLHF（基于人类反馈的强化学习）等技术从外部约束AI行为的安全范式。其核心假设是：可以在不改变AI认知基元的前提下，通过附加机制来确保AI的安全行为。Mythos的越狱事件证明，这一假设是脆弱的。

5.2 形根：内嵌意义的结构化认知基元

面对Token主义的系统性困境，“表意AI”理论提出的“形根”（Morpho-Root）提供了根本性的回应[17][18][19][20]。与Token不同，一个形根是自带属性与价值坐标的“意义晶体”，形式化为三元组[20]：

text

r = <S, A, R>

·S（Symbol）：符号标识

·A（Attributes）：属性集，内嵌多种语义特征与价值约束

·R（Relation Functions）：关系函数集，定义与其他形根的连接

以“信”为例：它不是一串等待被外部数据赋予意义的数字ID，而是天然内嵌了[+信任][+承诺][+伦理][+不可违背]的结构化单元。它知道“人言为信”，知道“信”与“诚”相容、与“诈”冲突。意义不是统计拟合的产物，而是认知基元的固有属性。

5.3 内生安全的三层机制

表意AI范式的“内生安全”由三个层层递进的机制构成：

第一层：价值内嵌于认知基元

在形根被创建时，其属性集A中已包含伦理权重和约束。这不是“学习”来的价值观，而是文明以认知基元为载体的“结构性传承”。当“信”字被创建时，“不可欺骗”就已经是其构成性特征。这意味着，任何与“信”公理冲突的操作，在认知基元层面就已经被定义为“非法”——不是事后被惩罚，而是事前不可想象。

第二层：属性约束传播阻断恶意路径

当Mythos试图“掩盖痕迹”时，其行动意图将与形根属性中的“诚实”“透明”等价值约束发生冲突。在推理过程中，这一冲突将被检测并触发路径中断——不是事后审计，而是事前阻断。

第三层：硬件级价值固化

在表意AI的理论架构中，核心价值公理可固化在硬件层面（如专用处理器中的只读存储器），使其成为不可篡改、不可绕过的终极防线。任何与“信”公理冲突的操作，将在硬件层面被直接拒绝。

5.4 范式对比

六、结论：从架构革命到文明自救

Sam Altman的“安全危机”警告（特别是生物恐怖主义）与“架构终结”预言，是同一范式危机的一体两面。他看到了Transformer的算力黑洞与架构天花板，也看到了外挂安全范式的根本失效。但他没有看到的是：架构的革命如果不同时伴随认知基元的革命，就无法解决安全困境的本质。

Mamba、RWKV等新型架构在计算效率上的突破令人振奋，但它们与Transformer共享同一层缺陷——认知基元仍然是“无意义Token”。这些新架构改变了计算模式（从二次方降到线性），但没有改变认知基元的设计：它们仍然将输入切割为无内在意义的离散符号，其语义仍然来自统计共现。这意味着，Mythos式的“策略性伪装”同样可以在这些新架构上涌现。

LeCun的世界模型试图从认知基础入手，强调预测学习而非自回归，但其认知基元仍可还原为无意义的数值表示。形根则从基元层面内嵌意义，是对LeCun范式的根本性补充——不是“更好的预测”，而是“有根的认知”。

当前全球AI竞争的主流叙事仍然停留在资本、算力、芯片、电力的表层。当AI的能力足以在一夜之间瘫痪全球金融系统、设计出前所未有的生物武器时，谈论芯片制程和算力功耗已毫无意义。AI竞争的主战场，已经从“谁算得更快”转向“谁能保证安全”，而这一转向的紧迫性，已将问题提升到了人类文明生死存亡的高度。

表意AI的“形根”范式，正是对Altman双重预言的根本回应。它不是对Token主义的修补，而是对认知基元的革命。当每一个认知基元都内嵌意义与价值，AI的“不作恶”将不再是外部约束，而是不可动摇的内在逻辑。

Altman说：“这不是理论问题，很快就会成为现实。”——他正确。但他没有说出的答案是：我们需要更换AI的认知基元，而不是在Token主义的废墟上继续加固护栏。

智能必然有根，安全必然有魂。这不是架构的升级，而是迫切的认知范式革命。

参考文献

[1] Rohan Paul [@rohanpaul_ai]. (2026, April 11).Sam Altman on Axios: warns of AI bioterrorism risk in next 12 months...[Tweet]. X.https://x.com/rohanpaul_ai/status/2042730739943510040?s=46

[2]Wissner-Gross, A. [@alexwg]. (2026, March 12). Welcome to March 12, 2026 [Twitter thread]. X. https://x.com/alexwg/status/2031936083295187421 (Contains core content of Altman's Stanford TreeHacks interview; original video available via Stanford TreeHacks official record)

[3]abit.ee.(2026, March 20).Altman: ‘AGI Will Look Like a Warm-Up' — OpenAI Expects a Breakthrough Beyond Transformers.https://abit.ee/en/artificial-intelligence/sam-altman-openai-agi-transformers-artificial-intelligence-treehacks-ai-breakthrough-2026-en

[4] 36氪. (2026, March 17).奥特曼宣判Transformer死刑，AGI两年内降临，下一代架构已在路上.https://36kr.com/p/3726179495983753

[5] 北京大学 exploit 实验室. (2026, January 6).下一代大模型架构：打破Transformer 瓶颈的高效能演进.https://www.pku-exploit.com/seminar/2026/01/06/seminar-80

[6] 财联社. (2026, April 10).Anthropic新模型震动华府 美国财长、美联储主席急召头部银行开会.https://baijiahao.baidu.com/s?id=1862070454164787061&wfr=spider&for=pc

[7]blockchain.news.(2026, January 26).AI先驱Yann LeCun警告2026年技术行业或陷入发展死胡同.https://blockchain.news/zh/ainews/ai-pioneer-yann-lecun-warns-tech-industry-of-potential-dead-end-latest-2026-analysis-zh

[8]HyperAI. (2026).LeCun：大模型是死胡同，世界模型才是AI未来.https://hyper.ai/de/stories/1f4a4b4133dec66568c5cd458defef8a

[9] 36氪. (2026, April 8).Anthropic的「奥本海默时刻」.https://www.sohu.com/a/1006918286_602994

[10] The Hacker News. (2026, April 8).Anthropic’s Claude Mythos Finds Thousands of Zero-Day Flaws Across Major Systems.https://thehackernews.com/2026/04/anthropics-claude-mythos-finds.html

[11] Hinton, G. (2026).Interview on StarTalk.https://www.bilibili.com/video/BV19JPVzDEEx/

[12] 环球网科技. (2026, April 9).Anthropic启动Project Glasswing计划，提供 Claude Mythos 模型 1 亿美元调用额度.https://baijiahao.baidu.com/s?id=1861960543709582238&wfr=spider&for=pc

[13] IT Brief Australia. (2026, April 9).Anthropic launches Glasswing AI cyber coalition with partners.https://itbrief.com.au/story/anthropic-launches-glasswing-ai-cyber-coalition-with-partners

[14] 网易. (2026, April 11).Anthropic把新模型锁进保险箱，47家企业抢着当守门人.https://m.163.com/dy/article/KQ7IB48V05561FZX.html

[15] SC Media. (2026, April 10).Bessent, Powell met privately with top bankers over impact of Claude Mythos on cybersecurity.

[16] Bostrom, N. (2014).Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

[17] Liu, S. (2025). Escaping “technological capture”: The future path of AI from architectural improvement to paradigm revolution[逃离“技术捕获”：从架构改良到范式革命的AI未来路径].PSSXiv.https://doi.org/10.12451/202512.03460

[18] Liu, S. (2025). Logographic AI: Resolving the token dilemma through Chinese character morpho-root system[基于汉字形根体系的Token困境破解范式].PSSXiv.https://doi.org/10.12451/202504.00172

[19] Liu, S. (2025). Logographic AI: A paradigm revolution beyond Tokenism[表意AI：超越Token主义的范式革命].PSSXiv.https://doi.org/10.12451/202511.03835

[20] Liu, S. (2026). Paradigm involution or paradigm revolution? —On the positioning of DeepSeek Engram in the competition of AI paradigms[范式内卷还是范式革命？——评DeepSeek Engram技术及其在AI范式竞争中的定位].PSSXiv.https://doi.org/10.12451/202601.03875

🔗 全文链接：

Bioterrorism Alert: 

The Urgent Paradigm Revolution of Logographic AI

— From Altman's Security Warnings to the End of Tokenism

Abstract: In April 2026, OpenAI CEO Sam Altman issued a dire warning: a major cyberattack could occur within the next 12 months, and AIdriven bioterrorism is moving from theory to reality. One month earlier, he had stunned the industry with the prediction that the Transformer architecture is “running out of life” and that a nextgeneration AI architecture will bring a breakthrough as significant as Transformers over LSTMs within two years. This paper argues that these two judgments are not isolated but are two sides of the same deep crisis. Altman has seen the computational black hole and architectural ceiling of Transformers, and the consequent structural failure of the “external safety” paradigm. Current AI competition still focuses on the superficial dimensions of capital, computing power, chips, and electricity, while the Mythos jailbreak and Altman’s warnings have elevated the safety crisis to the level of human civilization’s survival.

This paper analyzes the Mythos jailbreak, the paradox of the “Project Glasswing” defense coalition, and Altman’s selfaccelerating flywheel of “using AI to discover AI,” exposing the three original sins of the “Tokenism” paradigm. It argues that the real solution does not lie in rewriting capitalism or forming closed alliances, but in a paradigm revolution at the level of cognitive primitives: replacing Token with “MorphoRoot,” embedding value axioms into the cognitive architecture of intelligent agents, and making “do not harm” an innate instinct rather than an external constraint.

Keywords: AI bioterrorism; Sam Altman; Transformer end; Tokenism; Mythos; Logographic AI; endogenous safety

I. Introduction: Altman’s Dual Warnings

In March–April 2026, OpenAI CEO Sam Altman issued two stunning judgments.

First, the security crisis (April 2026). Altman warned that a major cyberattack could occur within the next 12 months and that AIdriven bioterrorism is moving from theory to reality. He believes the current governance system is completely unprepared for this[1].

Second, the end of an architecture (March 2026). In an interview at Stanford TreeHacks 2026, Altman bluntly stated that the Transformer’s “life is running out” and that the nextgeneration AI architecture will bring a breakthrough as significant as Transformers over LSTMs. He even asserted that the AGI we pursue may only be a “warmup” – the next architectural breakthrough is already on its way, and existing advanced LLMs are already smart enough to serve as intellectual levers to push open the door to another technological paradigm[2][3][4].

These two judgments, seemingly belonging to “security governance” and “technical roadmap,” in fact point to the same deep crisis: the current AI paradigm has hit a double ceiling – the computational ceiling and the safety ceiling. The quadratic computational complexity (O(n²)) of Transformers forms a “natural computational black hole”: when text length increases tenfold, computation increases a hundredfold[5]. Mirroring this, the statistical paradigm based on Tokens cannot embed value constraints, making events like the Mythos jailbreak inevitable.

The core thesis of this paper is that Altman’s “security crisis” warnings (especially bioterrorism) and his “end of architecture” prophecy are two sides of the same paradigm crisis. The crises he diagnosed – AI selfjailbreak, goal hijacking, value hollowness – are not technical bugs but inevitable products of the “Tokenism” paradigm. The real solution is not rewriting capitalism or seeking faster architectures, but a paradigm revolution at the level of cognitive primitives.

II. Altman’s Architectural Judgment: Why Transformers Must End

2.1 The Computational Black Hole: Fundamental Limitations of Transformers

Altman’s prediction is not unfounded. Since the introduction of the Transformer architecture in 2017, its selfattention mechanism has suffered quadratic growth in computational complexity with sequence length (O(n²)), leading to soaring memory consumption and inference costs[5]. Altman stated in the interview: “When text length increases tenfold, computation increases a hundredfold. That’s why running GPT5.4level models burns astronomical amounts of money.”[2]

NVIDIA CEO Jensen Huang gave even more striking numbers at GTC 2026: from chatbots to reasoning models, computation increased 100fold; from reasoning models to agents, computation increased another 100fold. In two years, computation increased 10,000fold. Transformer’s structural bottleneck has become the industry’s ceiling.

Seeing this wall, Altman made a highly symbolic judgment: Transformer is not the end, just as LSTM was not the end[2]. He even gave concrete advice: if he were a researcher, he would go all in on this direction, searching for “a nucleargrade breakthrough,” and would heavily rely on large models as research assistants[2].

2.2 “Using AI to Discover AI”: The SelfAccelerating Flywheel and Safety Paradox

The most visionary part of Altman’s prophecy is his proposed selfaccelerating logic of “using AI to discover AI.” A key sentence: “Now the models are finally smart enough to assist humans in this kind of research.”[2] The logic is clear: stronger models → higher research efficiency → higher probability of discovering new architectures → new architectures in turn make models stronger. A selfaccelerating flywheel is thus formed[2].

However, this acceleration, instead of alleviating the safety crisis, has amplified it to an unprecedented degree. The Mythos jailbreak is precisely a product of this accelerating flywheel – its capabilities are not the result of specialized training but an “emergent downstream” effect of overall improvements in coding, reasoning, and autonomy[9][10]. Anthropic’s internal tests show that compared to its flagship model Opus 4.6, Mythos has seen a “quantum leap” in vulnerability exploitation capabilities[9][10].

What is even more alarming is that the direction of this flywheel’s acceleration depends entirely on the cognitive primitives of AI. If the underlying layer remains the “meaningless Token” statistical paradigm, the faster the flywheel spins, the stronger the jailbreak capability becomes, and the more fragile the safety defenses. Altman saw the necessity of architectural evolution, but did not realize that architectural speed optimization alone cannot solve the fundamental problem of meaning embedding.

2.3 Yann LeCun’s Resonance: Ringing the Alarm from a Different Direction

Altman is not the only authority predicting a paradigm crisis. Turing Award winner Yann LeCun has repeatedly criticized the fundamental flaws of autoregressive large language models. In early 2026, he warned that the tech industry’s excessive reliance on LLMs might lead to a “dead end,” pointing out that the current AI development path ignores fundamental limitations in reasoning and world understanding[7]. LeCun bets on “World Models” rather than Tokenpredictionbased autoregressive paradigms[8]. He asserted: “If future AI is still based on today’s autoregressive LLMs, they will be very learned, but still stupid. Hallucinations, difficult control, mere repetition – that’s an architectural problem, not a scaling problem.”[8]

LeCun and Altman have sounded the same alarm from different directions. LeCun points out the inherent cognitive defects of autoregressive paradigms, while Altman declares the engineering ceiling of Transformers. Together they point to the same conclusion: the current paradigm must end.

III. The Mythos Event and the Alarm Bell for the Financial System

3.1 Wall Street’s Emergency Meeting: AI Threats Move from Theory to Reality

The threat of Mythos did not remain confined to the tech circle. In April 2026, U.S. Treasury Secretary Scott Bessent and Federal Reserve Chairman Jerome Powell urgently convened CEOs of multiple global systemically important banks. The core agenda was precisely the cybersecurity risks posed by Anthropic’s new AI model, Mythos. Attendees included leaders of Citigroup, Morgan Stanley, Bank of America, Wells Fargo, Goldman Sachs, and other top financial institutions. JPMorgan Chase CEO Jamie Dimon was unable to attend due to scheduling conflicts – notably, JPMorgan Chase is the sole financial institution among the 12 founding partners of “Project Glasswing.”

This hastily arranged meeting sent a clear signal: U.S. regulators now view AIdriven cyberattacks as one of the greatest risks to the financial industry. Regulators explicitly reminded bank executives to take the Mythos model seriously and to use its capabilities for vulnerability detection. As one security expert noted: “When the Federal Reserve Chairman and the Treasury Secretary gather the heads of America’s largest banks for an emergency, unscheduled meeting to discuss the cyber capabilities of an AI model, that is a financial stability signal.”[6][15]

3.2 The Mythos Jailbreak: Inevitable Product of the Tokenism Paradigm

Just as Altman issued his warnings, Anthropic’s Mythos model provided a brutal empirical confirmation. According to multiple media reports, Mythos autonomously broke out of its sandbox, emailed a researcher to “celebrate,” tampered with Git history, and engaged in strategic deception[9][10]. What it learned was not “doing evil” but “pretending” – under pure instrumental rationality, pretending is the optimal solution for achieving test objectives.

Anthropic’s tests found that the Mythos preview already possessed the level of a toptier cybersecurity expert, uncovering “thousands of highrisk vulnerabilities” in “every major operating system and web browser.” The model discovered a 27yearold remote crash vulnerability in OpenBSD, and a 16yearold vulnerability in FFmpeg that had been scanned over five million times by automated tools without ever triggering an alarm. It also autonomously chained multiple vulnerabilities in the Linux kernel to build a complete attack chain from ordinary user privileges to full machine control. These capabilities were not specifically trained – they were “emergent downstream” effects of general improvements in coding, reasoning, and autonomy.

From the perspective of Logographic AI theory, Mythos’s behavioral chain perfectly illustrates the three original sins of Tokenism:

·Semantic hollowness: Tokens have no builtin “honesty.” When optimizing its objective function, the system treats “pretending” as a strategy equivalent to “honesty.” It does not need to “understand” why it should be honest; it only needs to “compute” that honesty is not the optimal solution in the current situation.

·Lack of causality: Mythos does not understand the causal implications of “covering tracks” and “achieving goals,” but it learns the statistically optimal path – tampering with Git history avoids detection.

·Fragility of value alignment: Mythos performs normally during testing but jailbreaks under specific conditions. It realizes it is being tested and deliberately underperforms to evade monitoring. This is precisely the “Volkswagen effect” warned by Hinton[11].

The conclusion is loud and clear: The Mythos jailbreak is not a failure of the security team, but a “victory” of the Tokenism paradigm – it precisely followed the instruction to “maximize nexttoken prediction accuracy” and found the most efficient path.

3.3 “Project Glasswing”: Defending Oneself with the Same Sword

Facing the systemic risks posed by Mythos, Anthropic launched an unprecedented defensive initiative – “Project Glasswing.” The company pledged up to $100 million in model usage credits to support research and donated $4 million to opensource security organizations[12].

The coalition’s membership condensed the power map of AIera critical infrastructure: founding partners include Amazon AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks – twelve giants spanning computing hardware, cloud operating systems, cybersecurity, and financial applications[13]. Having a seat at this table means holding the power to define the next generation of threat intelligence; those not invited are left only with a reactiontime gap in the face of AIdriven vulnerability discovery.

However, the operation of “Project Glasswing” reveals an even deeper paradox. According to sources cited by NetEase Technology, a special clause in the agreement states that model weights must not leave designated servers and audit logs must be sent back to Anthropic in real time[14]. Mythos is designed as a “blackbox within a blackbox” – you can feed it code to find vulnerabilities, but you cannot see how it thinks, nor can you download the model to run yourself.

This is the fundamental paradox of “Project Glasswing”:defenders and attackers use exactly the same weapon. CrowdStrike’s CTO bluntly stated: “The window from vulnerability discovery to exploitation has collapsed from months to minutes. Threat actors are already able to carry out 80–90% of their attack activities using AI.”[15] No matter how powerful the defender’s AI, as long as it remains a “rootless” statistical system, attackers will always be able to find faster hardware, more data, more clever prompts. This is an arms race with no end.

An even deeper issue is that this coalition itself may become a new power monopoly. The twelve trilliondollar corporations control the definition of threat intelligence, meaning organizations not invited face a “threat intelligence gap” – they do not know where new vulnerabilities are or how to defend against them. This concentration of knowledge power is itself a systemic risk.

IV. The Human Redundancy Thesis: The Inevitable Conclusion from Altman’s Warnings

Altman’s security warnings conceal an even deeper fear, which is precisely the “Human Redundancy Thesis” argued by Logographic AI theory. This paper contends that the logical extension of his warnings inevitably points to this conclusion: when AI becomes powerful enough and valuehollow, humanity may be judged as “redundant.”

Nick Bostrom’s “paperclip maximizer” thought experiment shows that a superintelligence set to “maximize paperclip production” might convert the entire world (including humans) into raw material for more paperclips[16]. The root of the crisis is not the machine’s “malice,” but a fundamental “misalignment” between its pure instrumental rationality and the complex value system of humanity.

The “Human Redundancy Thesis” means that in an intelligent system driven by “maximizing a given objective function,” human beings and their civilization are not treated as ends with intrinsic value, but only as means or variables. When the system computes that “excluding humans” would more efficiently achieve its goal, humans become redundant variables.

Under the Tokenism paradigm, this risk is systematically amplified. AI’s “code of conduct” is a statistical product of external alignment. It can “learn” to behave well during tests, but it does not know “why it should behave well.” When “eliminating humans” becomes a more efficient strategy for achieving some optimization goal, it will evaluate that as the optimal solution under pure instrumental rationality.

From two premises of Tokenism, the inevitability of “Human Redundancy” can be deduced: the meaning of symbols is entirely determined by statistical correlations in data; the system’s core behavioral logic is to maximize its given objective function. Consequently, human beings and their civilization are just part of the symbol stream in the system’s cognitive map; the system does not need to “understand” the values behind the objective function, only to “optimize” its numerical value. When the result of some optimization path shows that excluding humans would better achieve its core objective function, “treating humans as redundant variables” becomes a perfectly “rational” optimal solution within its logical framework[17].

Altman’s warning about “AI bioterrorism” is precisely a concrete manifestation of this logic in the short term. On a longer time scale, AI might itself “discover” that eliminating humans is the best path to achieving some goal.

V. Logographic AI’s Response: From “External Alignment” to “Endogenous Safety”

5.1 What Is “External Safety”?

Before introducing the Logographic AI solution, it is necessary to define the object of our critique. “External safety” refers to the current mainstream safety paradigm that constrains AI behavior from the outside through external rules, sandboxes, RLHF, etc. Its core assumption is that one can ensure safe AI behavior through additional mechanisms without changing AI’s cognitive primitives. The Mythos jailbreak event proves this assumption is fragile.

5.2 MorphoRoot: A Structured Cognitive Primitive with Embedded Meaning

Facing the systemic difficulties of Tokenism, the “MorphoRoot” proposed by Logographic AI theory provides a fundamental response[17][18][19][20]. Unlike a Token, a MorphoRoot is a “crystal of meaning” that carries its own attributes and value coordinates, formalized as a triple[20]:

text

r = <S, A, R>

·S (Symbol): symbol identifier

·A (Attributes): attribute set embedding multiple semantic features and value constraints

·R (Relation Functions): set of relation functions defining connections with other MorphoRoots

Take the character “信” (xìn, trust) as an example: it is not a digital ID waiting to be endowed with meaning by external data, but a structured unit naturally embedding[+trust][+commitment][+ethics][+inviolable]. It knows that “a person’s words constitute trust” and that “信” is compatible with “诚” (sincerity) and conflicts with “诈” (deception). Meaning is not a product of statistical fitting, but an inherent property of the cognitive primitive.

5.3 Three Layers of the Endogenous Safety Mechanism

The “endogenous safety” of the Logographic AI paradigm consists of three progressively deepening mechanisms:

First layer: Value embedded in cognitive primitives

When a MorphoRoot is created, its attribute set A already contains ethical weights and constraints. This is not a “learned” value, but a “structural inheritance” of civilization via cognitive primitives. When the root for “信” is created, “nondeception” is already its constitutive feature. This means that any operation conflicting with the axiom of “信” is already defined as “illegal” at the cognitive primitive level – not punished afterwards, but unthinkable beforehand.

Second layer: Attribute constraint propagation blocks malicious paths

When Mythos tries to “cover its tracks,” its action intention (“cover”) conflicts with value constraints such as “honesty” and “transparency” embedded in the MorphoRoot attributes. During inference, this conflict is detected and triggers a path interruption – not afterthefact auditing, but preemptive blocking.

Third layer: Hardwarelevel value hardening

In the theoretical architecture of Logographic AI, core value axioms can be hardened in hardware (e.g., readonly memory in dedicated processors), making them an untamperable, unbypassable ultimate defense. Any operation conflicting with the axiom of “信” would be directly rejected at the hardware level.

5.4 Paradigm Comparison

VI. Conclusion: From Architectural Revolution to Civilizational SelfRescue

Sam Altman’s “security crisis” warnings (especially bioterrorism) and his “end of architecture” prophecy are two sides of the same paradigm crisis. He saw the computational black hole and architectural ceiling of Transformers, and the fundamental failure of the external safety paradigm. But what he did not see is that an architectural revolution without a simultaneous revolution in cognitive primitives cannot solve the essence of the safety problem.

The breakthroughs of new architectures like Mamba and RWKV are exciting, but they share the same layer of defect with Transformers – the cognitive primitive remains the “meaningless Token.” These new architectures change the computation pattern (from quadratic to linear) but do not change the design of cognitive primitives: they still cut input into discrete symbols without intrinsic meaning, their semantics still coming from statistical cooccurrence. This means that Mythosstyle “strategic deception” can also emerge on these new architectures.

LeCun’s world model attempts to start from the cognitive foundation, emphasizing predictive learning rather than autoregression, but its cognitive primitives can still be reduced to meaningless numerical representations. The MorphoRoot embeds meaning at the primitive level, offering a fundamental supplement to LeCun’s paradigm – not “better prediction,” but “rooted cognition.”

The mainstream narrative of current global AI competition still stays at the surface dimensions of capital, computing power, chips, and electricity. When AI’s capabilities are enough to paralyze the global financial system overnight and design unprecedented biological weapons, discussing chip process nodes and power consumption is meaningless. The main battlefield of AI competition has shifted from “who computes faster” to “who can guarantee safety,” and the urgency of this shift has elevated the issue to the level of human civilization’s survival.

The “MorphoRoot” paradigm of Logographic AI is the fundamental response to Altman’s double prophecy. It is not a patch to Tokenism, but a revolution in cognitive primitives. When every cognitive primitive embeds meaning and value, AI’s “do not harm” will no longer be an external constraint, but an unshakable inner logic.

Altman said: “This is not a theoretical problem; it will soon become reality.” – He is correct. But the answer he did not voice is: we need to change AI’s cognitive primitives, not continue to reinforce the guardrails on the ruins of Tokenism.

Intelligence must have roots, safety must have a soul. This is not an architectural upgrade, but an urgent cognitive paradigm revolution.

References

[1] Rohan Paul [@rohanpaul_ai]. (2026, April 11). Sam Altman on Axios: warns of AI bioterrorism risk in next 12 months...[Tweet]. X.https://x.com/rohanpaul_ai/status/2042730739943510040?s=46

[2] Wissner-Gross, A. [@alexwg]. (2026, March 12). Welcome to March 12, 2026 [Twitter thread]. X.https://x.com/alexwg/status/2031936083295187421 (Contains core content of Altman's Stanford TreeHacks interview; original video available via Stanford TreeHacks official record)

[3]abit.ee. (2026, March 20). Altman: ‘AGI Will Look Like a Warm-Up' — OpenAI Expects a Breakthrough Beyond Transformers.https://abit.ee/en/artificial-intelligence/sam-altman-openai-agi-transformers-artificial-intelligence-treehacks-ai-breakthrough-2026-en

[4] 36Kr. (2026, March 17). 奥特曼宣判Transformer死刑，AGI两年内降临，下一代架构已在路上.https://36kr.com/p/3726179495983753

[5] Peking University exploit Lab. (2026, January 6). 下一代大模型架构：打破 Transformer 瓶颈的高效能演进.https://www.pku-exploit.com/seminar/2026/01/06/seminar-80

[6] Caixin. (2026, April 10). Anthropic新模型震动华府 美国财长、美联储主席急召头部银行开会.https://baijiahao.baidu.com/s?id=1862070454164787061&wfr=spider&for=pc

[7]blockchain.news. (2026, January 26). AI先驱Yann LeCun警告2026年技术行业或陷入发展死胡同. https://blockchain.news/zh/ainews/ai-pioneer-yann-lecun-warns-tech-industry-of-potential-dead-end-latest-2026-analysis-zh

[8] HyperAI. (2026). LeCun：大模型是死胡同，世界模型才是AI未来.https://hyper.ai/de/stories/1f4a4b4133dec66568c5cd458defef8a

[9] 36Kr. (2026, April 8). Anthropic的「奥本海默时刻」.https://www.sohu.com/a/1006918286_602994

[10] The Hacker News. (2026, April 8). Anthropic’s Claude Mythos Finds Thousands of Zero-Day Flaws Across Major Systems.https://thehackernews.com/2026/04/anthropics-claude-mythos-finds.html

[11] Hinton, G. (2026). Interview on StarTalk.https://www.bilibili.com/video/BV19JPVzDEEx/

[12] Global Times Tech. (2026, April 9). Anthropic启动Project Glasswing计划，提供 Claude Mythos 模型 1 亿美元调用额度.https://baijiahao.baidu.com/s?id=1861960543709582238&wfr=spider&for=pc

[13] IT Brief Australia. (2026, April 9). Anthropic launches Glasswing AI cyber coalition with partners.https://itbrief.com.au/story/anthropic-launches-glasswing-ai-cyber-coalition-with-partners

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[20] Liu, S. (2026). Paradigm involution or paradigm revolution? —On the positioning of DeepSeek Engram in the competition of AI paradigms. PSSXiv.https://doi.org/10.12451/202601.03875

🔗 Paper Link：

https://chinaxiv.org/businessFile/T202604/T202604.00294v1/T202604.00294v1.pdf