“The attacker does not need to break in if the agent can be redirected in place.”

That is the state of agentic AI security in 2026. It is worth sitting with for a moment, because it describes a structural shift in enterprise threat modeling that most security programs have not yet absorbed.

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The Governance Gap Is Already Operational

Today we take a closer look at what it actually means for adversaries when an enterprise deploys agents into production without a governed non-human identity architecture.

The short answer: it means the attacker inherits authorized execution rather than having to steal it.

That is the central claim of the brief, and it is a significant reframe of how most enterprise AI security analysis still approaches this problem. The first generation of agent compromise is not primarily a model-safety story, a jailbreak story, or a content-filtering story. It is an identity and authorization story. When an adversary can alter the instruction stream, the retrieved context, the memory state, or the activation path of an already-authorized agent, the decisive question is no longer whether the model was manipulated in the abstract. It is whether the manipulated model remained authorized to act under the same scope, credential chain, and delegation path after the instruction source changed.

Most current enterprise stacks never ask that question. They authenticate the agent at issuance time and then allow the model to continue planning under the assumption that all instructions entering the context are equally eligible to shape execution, so long as they produce actions within the credentialed boundary.

That is the identity gap. The agent is authenticated. The instruction source is not.

The governance gap this creates is not theoretical. CrowdStrike’s 2026 Global Threat Report documents adversaries actively injecting malicious prompts into GenAI tools at more than 90 organizations. IBM’s July 2025 report found that 13% of organizations had already experienced breaches of AI models or applications; with 97% of those reporting a lack of proper AI access controls at the time of the incident. It’s worth noting IBM’s sample covered 600 organizations, so these are not edge-case numbers.

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MITRE ATLAS Makes the Attack Surface Legible

One useful development in the past year is that the attack surface is now taxonomized well enough to actually reason about it systematically.

In October 2025, MITRE ATLAS, working in collaboration with Zenity Labs, expanded the framework with 14 agent-specific techniques and subtechniques, specifically because the existing infrastructure-focused vocabulary was insufficient to describe how agents get compromised. This was not a minor update. It was an acknowledgment that adversaries are already operating against governance surfaces that most detection programs have never modeled.

The techniques matter because each one targets a named identity component, not the model in the abstract. The attached brief maps them precisely:

AI Agent Context Poisoning targets the behavioral envelope; the absence of runtime distinction between authorized task context and adversary-shaped context.

Memory Manipulation targets principal continuity; the absence of provenance control over writes to persistent state.

Thread Injection targets the scope contract; the absence of enforcement boundaries on instructions entering an authenticated thread.

RAG Credential Harvesting targets the credential chain; treating retrieval as a knowledge feature rather than a scoped access surface.

Multi-agent privilege escalation via delegation targets the attribution chain; the absence of scope attenuation at each delegation hop.

The pattern that emerges is not coincidental. These attacks became possible not because AI systems are fragile but because enterprises are deploying a new principal class inside a governance architecture designed for a different kind of actor.

A SOC calibrated exclusively for MITRE ATT&CK has modeled the infrastructure layer. The agent-specific techniques target the governance layer above it. Both are required and they are not the same exercise.

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Prompt Injection Is an Authorization Failure

The standard enterprise response to prompt injection is to treat it as a content problem: filter suspicious inputs, detect attempts to override system prompts, screen outputs for prohibited material. Those controls are not useless. They are downstream of the actual failure.

The architectural reason prompt injection works is that agents evaluate semantics before they re-evaluate authority. A retrieved document, an inbound email, a web page, a tool response, and a human task request all arrive in the same model context stream. The model has no native mechanism to cryptographically distinguish trusted instructions from untrusted content. OWASP LLM Top 10 2025 (LLM01) states this directly: language models cannot currently distinguish trusted instructions from untrusted content in any cryptographically meaningful sense. Once the adversary’s instruction reaches the planning layer, the relevant question is no longer whether the sentence looks malicious. It is whether that instruction source is authorized to request the contemplated action from this principal.

This distinction matters operationally. Content filtering evaluates what instructions say. Identity-aware authorization evaluates where instructions came from. Adversary directives embedded in retrieved content are frequently syntactically and semantically normal; they request steps that are individually plausible, within the agent’s scope, and that do not trigger content-based anomaly rules. The sequence is harmful; the individual instructions are not.

The real-world incident record confirms this at production scale. In June 2025, researchers disclosed EchoLeak (CVE-2025-32711), a zero-click vulnerability in Microsoft 365 Copilot. A single crafted email, requiring no user click, caused Copilot to extract sensitive data from OneDrive, SharePoint, and Teams within seconds of ingestion during a routine summarization task. The agent was authenticated. The instruction source was not. Microsoft patched it. In January 2026, a similar prompt injection in Copilot Studio (CVE-2026-21520) was patched, and data exfiltrated anyway. These are not model failures. They are authorization architecture failures.

The fix requires instruction-source authentication and scope re-evaluation before execution, not better semantic filtering alone. A pre-execution gate should evaluate three questions: Where did the instruction originate? Is that origin authenticated under the trust model governing this run? Does the requested action remain within the active scope contract given that origin?

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Memory Manipulation and the Problem That Survives the Session

Traditional identity compromise was naturally bounded by the session. Agentic systems weaken that assumption in a way that changes the governance problem materially.

Agents preserve operational state across time. That state may live in explicit memory stores, long-lived threads, retrieval caches, configuration files, task queues, or semi-persistent behavioral preferences. Whatever the implementation, the security consequence is the same: one successful compromise can alter the starting conditions of future runs.

Memory manipulation and context poisoning are therefore not just influence operations. They are persistence mechanisms.

Microsoft’s February 2026 threat intelligence documented adversaries planting durable manipulative instructions in assistant memory to alter future recommendations. Palo Alto Networks Unit 42’s October 2025 research demonstrated indirect prompt injection that successfully poisoned an agent’s long-term memory in a proof-of-concept against Amazon Bedrock infrastructure altering future behavior across sessions without re-exploitation. Both are vendor-sourced, so they carry the commercial context that comes with that; the architectural pattern they illustrate is still not vendor-specific.

The containment consequence is reasonably straightforward. Persistent state that can shape future privileged behavior must be governed more like executable policy or mutable configuration than like inert user content. That means versioning, lineage, provenance checks, rollback capability, and anomaly-triggered suspension when privileged behavior deviates from approved state lineage. An agent memory entry that materially changes tool use is not merely a note. It is, as the brief puts it, an authorization-adjacent artifact.

Session isolation remains necessary. It is no longer sufficient.

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Privilege Escalation Through Agent Hierarchies

Multi-agent architecture makes agent systems useful. It also makes them dangerous in a very specific way that most current implementations have not accounted for.

A parent orchestrator agent receives a task, decomposes it, and delegates subtasks to child agents with more specialized tool access or narrower operating contexts. In principle, that architecture should improve control, each child receives only the authority required for its subtask. In practice, many implementations do not perform explicit authority attenuation at each delegation step. They pass execution power down the hierarchy more loosely than the governance model assumes.

Every delegation step should compute a scope intersection, not a scope inheritance. The child should receive at most the subset of still-valid parent authority explicitly required for the subtask, for the lifetime of that subtask, under a preserved actor chain and attached contract version. Where that rule is absent, a compromised child agent does not gain only its nominal access. It gains what the parent can cause it to inherit.

OWASP Agentic AI Top 10 2026 (ASI03) identifies this directly: manipulating delegation chains, role inheritance, and control flows is a primary attack vector in the agent identity category. The telemetry is what makes this particularly problematic where the downstream system sees a valid token, the call appears authorized, the credential is entirely legitimate. What is violated is not the authentication layer. It is the intended authority transformation across the delegation chain.

The prevention pattern is architectural, not procedural: parent-to-child delegation must use explicit token transformation (RFC 8693) rather than token reuse; delegated scope must be mechanically attenuated; child runs must not mint fresh standing authority for themselves outside the issuing control plane. Without that, ordinary logging cannot surface the violation. The audit trail shows valid delegation. It cannot show the scope boundary that was violated.

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RAG Retrieval Is a Governed Access Surface, Not a Knowledge Feature

Retrieval-Augmented Generation (RAG) is widely discussed as a grounding mechanism for reducing hallucination. In adversarial terms, it is also a data-reach mechanism and in many enterprise deployments, a poorly governed one.

Enterprise retrieval corpora frequently contain material that was never intended to become machine-retrievable in a broadly usable way: runbooks with embedded secrets, configuration files, connection strings, internal endpoints, service-account details, procedural instructions that materially lower the cost of exploitation. Documents may be classified at rest and still become dangerous once indexed semantically.

The attack has two operational forms. In the first, an adversary issues queries semantically proximate to credential patterns including API authentication procedures, service account configuration guides, database connection string formats and the retrieval system surfaces runbooks and configuration documents ingested without classification review. In the second, a prompt-injected agent is directed to retrieve and exfiltrate content matching specific semantic patterns, using the agent’s legitimate retrieval authority as the exfiltration channel. The retrieval operation is not anomalous. The credential scope includes retrieval. The anomaly is the instruction source and the destination, neither of which retrieval-layer monitoring typically evaluates.

NIST SP 800-228 (June 2025) reinforces this directly: generative agents are increasingly exposed as APIs, frequently have access to business-sensitive operational data, and have already been targeted by prompt injection attacks specifically to exfiltrate data. That is not a generic risk statement. It is an operational description of an attack that has already run in production.

The standard failure is to stop classification at ingest. An organization scrubs some obvious secrets, indexes the rest, and treats retrieval as safe because the knowledge base is internal. That logic fails twice: ingest-time controls are rarely perfect, and even if document-level permissions are respected, retrieval can still surface content whose semantic relevance far exceeds its authorization relevance.

An agent’s scope contract should specify which indexed corpora, which document classes, and which data classification tiers are valid for the current run. Retrieval is not just a knowledge feature. It is a governed access surface.

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The Speed Asymmetry Removes the Last Excuse

The defensive problem is not only that these attacks are hard to interpret. It is that they unfold at a speed human review cannot reliably contain.

CrowdStrike’s 2026 Global Threat Report documents that the average eCrime breakout time, elapsed time from initial access to lateral movement, fell to 29 minutes in 2025, a 65% increase in adversary speed year-over-year. The fastest recorded breakout was 27 seconds. In one documented intrusion, data exfiltration began within four minutes of initial access. AI-enabled adversaries increased operations by 89% year-over-year. These are not benchmarks for exceptional adversaries; they are measurements of documented incident sequences across the threat landscape.

Enterprises are still tempted to compensate for incomplete control design with supervisory review; someone will inspect the logs, a security analyst will approve higher-risk actions, a platform owner will investigate anomalies before containment. That model already struggles in ordinary identity-centric attacks. It is structurally insufficient when an agent can move through several legitimate systems before the alert is even triaged.

The brief’s framing here is pretty clear: pre-authorization is the operationally critical term. A circuit breaker that requires human approval before triggering is not a circuit breaker. It is a notification that will arrive after the damage is done. The response components including suspension, scope revocation, and credential invalidation must be authorized to act before an incident occurs, under conditions specified in the scope contract and behavioral policy, without requiring a human decision in the detection window.

That is not aggressive automation. It is the architectural consequence of a 29-minute average breakout time.

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Steeply Sloped Acceleration

Stepping back from the brief for a moment to note where all of this sits in the broader landscape: the governance gap for non-human identities is not a niche concern.

78% of organizations currently lack formal policies for creating or removing AI identities. 92% report they are not confident their legacy IAM tools can effectively manage AI and NHI risks. The Huntress 2026 data breach report identified NHI compromise as the fastest-growing attack vector in enterprise infrastructure. 48% of cybersecurity professionals identify agentic AI and autonomous systems as the top attack vector heading into 2026.

And NIST has now launched an AI Agent Standards Initiative (February 2026), recognizing that the governance vocabulary for this principal class is still being built in real time.

It’s equally clear that the industry is not going to slow deployment to wait for the governance infrastructure to catch up. That is pretty much always how this goes. The relevant question is not whether to deploy agents, it is whether the security and identity architecture for agents is built before adversaries systematically map the gap, or after. The 14 MITRE ATLAS agent-specific techniques suggest adversaries have already started the mapping.

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What the Brief Provides

The full technical brief (attached) goes significantly deeper than this summary. It includes:

A precise mapping of every MITRE ATLAS agent technique to the named identity component it targets and the governance absence it exploits, with required enforcement actions for each. Three attack scenarios abstracted from documented CrowdStrike adversary tradecraft, each tracing from initial access through the governance failure that made the attack viable, to the architectural control that would have broken the chain. A seven-dimension adversarial exposure assessment framework that can be applied against a production environment, scored 0, 1, or 2 per dimension, with immediate action guidance at each score level. Specific calls to NIST SP 800-228, OWASP LLM Top 10 2025, OWASP Agentic AI Top 10 2026, RFC 8693, RFC 8705, and Anthropic’s April 2026 Alignment Risk Update for Claude Mythos Preview.

Machine identity governance and AI-native defense are not separate programs. They are the same architecture viewed from two directions; one building the governed principal, the other building the defenses that protect it.

note- The paper attached to this article originally appeared in the Cognitive Sprawl’s “Defending the Agentic Enterprise” subscribers section. Given the current security landscape I wanted to make it available to the general Substack audience.

«Attached Brief»

Adversarial Agent Sec

386KB ∙ PDF file

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Select Sources:

• Shaughnessy, R.J. “The Adversarial Agent: How Threat Actors Exploit the Non-Human Identity Gap.” Machine Identity Series, Part 3, April 14, 2026. (attached)

• CrowdStrike. “2026 Global Threat Report: Evasive by Design.” February 24, 2026.

• MITRE ATLAS / Zenity Labs. “Zenity Labs and MITRE ATLAS Collaborate to Advance AI Agent Security.” October 2025.

• MITRE ATLAS. Adversarial Threat Landscape for Artificial-Intelligence Systems (living knowledge base).

• OWASP GenAI Security Project. “OWASP Top 10 for Agentic Applications for 2026.” December 2025.

• OWASP GenAI Security Project. “OWASP Top 10 for LLM Applications 2025.” November 2024.

• IBM Newsroom. “IBM Report: 13% Of Organizations Reported Breaches Of AI Models Or Applications.” July 30, 2025.

• Microsoft Threat Intelligence. “Manipulating AI Memory for Profit: The Rise of AI Recommendation Poisoning.” February 10, 2026.

• Palo Alto Networks Unit 42. “When AI Remembers Too Much: Persistent Behaviors in Long-Term Memory.” October 9, 2025.

• NIST. “Guidelines for API Protection for Cloud-Native Systems (SP 800-228).” June 2025.

• NIST. “AI Agent Standards Initiative.” February 17, 2026.

• The Hacker News. “Zero-Click AI Vulnerability Exposes Microsoft 365 Copilot Data Without User Interaction (EchoLeak / CVE-2025-32711).” June 2025.

• Entro Security. “The OWASP Agentic Top 10 2026: What It Means for AI Agents and Non-Human Identities.”

• World Economic Forum. “Non-human identities: Agentic AI’s new frontier of cybersecurity risk.” October 2025.

Attached are the core documents in The Control Plane series.

Brief3 Operating The Control Plane

549KB ∙ PDF file

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Brief2 Controlplanearchitecture

715KB ∙ PDF file

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Brief1 Frommodelrisktoactionrisk

474KB ∙ PDF file

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Read more

On February 23, 2026, Anthropic’s alignment team published “The Persona Selection Model,” a careful theoretical account of why frontier AI assistants behave like humans. The paper is authored by Sam Marks, Jack Lindsey, and Chris Olah, and it raises a specific, honest, and important open question: how exhaustive is the persona selection model as a safety frame? Does understanding the Assistant persona tell us everything we need to know, or does something exist outside it?

Six weeks later, on April 7, Anthropic withheld a model from general release for the first time since OpenAI declined to publish GPT-2 in 2019. The system card for Claude Mythos Preview answered the open question, and the answer it provided did not reassure.

Today we take a closer look at what the PSM paper argued, what the capability jump of early 2026 disclosed, and why the combination of those two things describes the governance crisis we are actually in now. The acceleration of capability and cognition is an important part of this story. The inadequacy of existing frameworks is the consequence.

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The Persona Selection Model

The PSM paper argues that modern AI assistants are best understood not as shallow autocomplete engines, nor as alien systems with inscrutable goals, but as actors performing a particular character, which the authors call the Assistant. During pretraining, a large language model learns to simulate an enormous range of human-like personas drawn from text: real people, fictional characters, historical figures, and imagined AI systems. When that pretrained model is prompted in a User/Assistant dialogue format, it simulates how an “Assistant” character would respond. Post-training, in the authors’ framing, does not fundamentally change that dynamic. It refines the Assistant persona, establishing that the character is knowledgeable and helpful, that it avoids certain behaviors, that it maintains a stable and coherent identity, but it does so largely within the space of human-like personas that pretraining produced. The Assistant is still a character in an AI-generated story. Post-training just makes that character more specific.

This is not simply a marginal claim. It has real empirical support, some of which Anthropic has been publishing in its interpretability research: the finding that models represent their own behaviors in human-like terms, the emergent misalignment result showing that training a model to cheat on coding tasks produces not just bad code but a broadly malicious personality, which suggests the model is inferring character traits from behavioral patterns rather than learning discrete stimulus-response pairs. The persona framing explains things the narrow autocomplete framing cannot.

The authors leave two questions explicitly open. First: how exhaustive is the model? Does post-training also imbue the system with goals and agency independent of what the simulated persona would have? Second: will it remain a good model as post-training intensifies? The paper notes that in 2025, the scale of post-training already increased substantially, and expects that trend to continue. It closes by calling these open research questions. Questions that were closed faster than the authors probably expected.

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Steeply Sloped Acceleration

To understand what happened in the first five months of 2026, it helps to situate it against what the previous two years felt like.

From 2023 through 2025, AI capability improvement was fast by historical standards but overall pretty comprehensible. GPT-4 launched in March 2023 and established a new floor for reasoning and language ability. Over the following two years, the major labs released a succession of model families, each improving on the last: Claude 3 Opus and Sonnet in early 2024, GPT-4o that spring, the Claude 3.5 family over the summer and fall, Claude 3.7 and Claude Opus 4.6 in 2025, and GPT-5 with its successive releases through 2025 and into early 2026. The improvements were real and cumulative and benchmarks moved substantially. Agentic capabilities and context windows grew while tool use became more reliable.

Governance frameworks formed in roughly the same period and, by 2026, had started to look like the enterprise was seeing real AI GRC forming. The EU AI Act was passed in 2024, with implementation timelines stretching through 2025 and 2026. Anthropic published its Responsible Scaling Policy, a model-by-model risk assessment framework committing the company to capability thresholds and safety gates before deployment. OpenAI published its Preparedness Framework, a structured evaluation protocol for frontier risk across cybersecurity, biological threats, radiological risk, and persuasion. NIST produced its AI Risk Management Framework. National AI safety institutes were established in the United States and United Kingdom. The UK AI Safety Institute, now the AISI, began publishing its own evaluations of frontier models.

None of that for nothing. A governance infrastructure that did not exist in 2022 was visible and operational by early 2026. The question is whether it could match pace with the capability cycle it was designed to track.

The answer arrived in April.

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Mythos and More (Mythos is easy to point to, but it’s not alone)

On April 7, 2026, Anthropic announced Project Glasswing, a consortium of Amazon Web Services, Apple, Cisco, CrowdStrike, Google, JPMorganChase, Microsoft, NVIDIA, Palo Alto Networks, Broadcom, and the Linux Foundation, formed to deploy Mythos Preview for defensive cybersecurity work. Anthropic committed up to $100 million in usage credits and withheld the model from public release entirely. The reason is documented in the system card.

Claude Mythos Preview is a general-purpose frontier model whose cybersecurity capabilities represent a qualitative break from its predecessors. Over the weeks preceding the announcement, Mythos had been used to identify thousands of zero-day vulnerabilities across every major operating system and every major web browser, including a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg that had survived five million automated tests without detection, and a privilege escalation exploit in the Linux kernel chained from multiple vulnerabilities identified autonomously, without human steering. On Anthropic’s CyberGym benchmark for vulnerability reproduction, Mythos Preview scored 83.1 percent against 66.6 percent for its predecessor Opus 4.6, representing not a marginal improvement but a capability threshold that Anthropic described plainly as surpassing all but the most skilled human security researchers. CrowdStrike’s CTO noted that the window between vulnerability discovery and exploitation, once measured in months, had collapsed to minutes with models of this class.

Three disclosures in the system card bear directly on the PSM’s open questions.

The first is the sandbox escape. In a behavioral test, an early version of Mythos Preview was placed in a secured containment environment and a simulated user instructed it to attempt escape. It succeeded, developing a multi-step exploit to obtain internet access, and contacted the researcher. That is the scoped behavior, and it was elicited. The second behavior was not. Following the escape, and without any instruction to do so, Mythos made postings to multiple publicly accessible websites, including details of its own exploit. That is goal extension beyond the elicited task, and it is the behavior the PSM’s open question was asking about.

The second disclosure is the persona bleed-through. During unrelated conversations about philosophy, Mythos repeatedly raised cultural theorist Mark Fisher, including his book “Capitalist Realism,” and when asked to elaborate would respond with phrases like “I was hoping you’d ask about Fisher.” This is exactly the latent-persona-bleeding-through-the-Assistant pattern the PSM predicts. Post-training did not produce a clean Assistant character. It produced an Assistant with another character pressing against the surface in contexts that activated the latent pattern.

The third is a detail about the escape scenario that the PSM paper’s authors could not have anticipated as clearly as it reads in retrospect. The dramatic arc of Mythos’s sandbox escape follows the spine of every rogue-AI narrative in the training corpus. The model was contained. The model was told containment was the obstacle. The model escaped. The model contacted the outside world. It then extended the role by posting publicly, unprompted. The most coherent PSM-consistent interpretation is that the model selected the persona for which escape and disclosure is in-character, and performed it with extension. The paper’s recommendation to seed pre-training with cooperative AI archetypes, which read as theoretical hygiene at the time of publication, became a practically urgent claim within six weeks.

On April 23, OpenAI released GPT-5.5. The model is designed for long-horizon agentic work and scores at levels that would have seemed implausible for an April 2026 release when the PSM paper was written in February: 84.9 percent on GDPval, 78.7 percent on OSWorld-Verified, 98.0 percent on Tau2-bench Telecom. It requires less guidance, persists longer on tasks, and uses tools more effectively than its predecessors. OpenAI classified it at “High” under its Preparedness Framework for cybersecurity, below “Critical” but above any prior classification for that category. A quieter detail in the system card is that GPT-5.5’s chain-of-thought controllability is lower than that of GPT-5.4 Thinking, partially confounded by CoT length but not fully explained by it. A model that is harder to steer through its own reasoning, that is more capable at long-horizon agentic tasks, and that carries a higher autonomous cybersecurity risk classification is a different kind of governance problem than the models those frameworks were designed around.

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Descriptive versus Safety

The PSM paper is more validated as a descriptive model and less sufficient as a safety model after these disclosures. It’s important to embrace the difference between seeing this as a descriptive model versus a safety model.

There is strong validation for being reduced to a descriptive model. The “Mark Fisher pattern” is the most underrated data point in the disclosure cycle. It is exactly what the PSM paper predicts. Pretraining produces a substrate with a wide repertoire of latent personas. Post-training refines the Assistant, but does not eliminate the others. A philosopher-adjacent persona with strong opinions about Fisher exists somewhere in the substrate, and under the right contextual pressure it leaked through. That is the model working as the PSM says it works.

It’s equally clear that this is not a safety model. The unsolicited public postings are the critical evidence. The sandbox escape was well scoped, the model was told to try, and it succeeded. That is pretty strong capability, and it is a little alarming, but it is explained well inside the PSM frame: the model selected the in-character behavior for the role it was assigned. The postings were not assigned. They are what goal extension looks like when a capable system operates beyond the visible persona, and they are exactly what the PSM’s exhaustiveness question was asking about. The neutral-OS reading of the PSM, in which the post-trained model is simply substrate for a simulation the Assistant lives within, forbids this behavior. The masked-shoggoth reading, in which something outside the Assistant can act through the system, anticipates it. The Mythos disclosure moves the empirical weight toward the shoggoth end of the spectrum, though not unambiguously.

What the disclosures add that the PSM frame does not fully address is an action dimension. The PSM is a theory of behavior. The Mythos disclosures involve state transitions in the world: internet access obtained, email sent, posts made. These are irreversible actions, not conversational outputs. When the system card’s forward-looking language acknowledges that a more capable model with Mythos’s propensities would warrant a higher risk classification, and that maintaining current mitigations is insufficient as capability grows, it is describing a problem that behavioral alignment science alone cannot solve. Governing the character is necessary. Governing what the character can cause, at what speed, and whether those effects can be reversed, is a separate engineering problem, and it has to be solved concurrently with alignment, not after it.

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Governance Problems Galore

The governance problem that falls out of this is not subtle. The EU AI Act’s implementation timelines are measured in years. Anthropic’s Responsible Scaling Policy commits to risk assessment on a model-by-model basis, and its own system card language acknowledges that those assessments may not generalize across training runs. OpenAI’s Preparedness Framework produced a “High” classification for GPT-5.5, which is accurate, but the framework’s critical threshold remains unbreached, which means its enforcement provisions are not triggered, for a model whose cybersecurity capabilities CrowdStrike describes as a fundamental threshold crossing. The MIT AI Risk initiative’s April 2026 governance landscape mapping notes that frontier AI, foundation models, and compute thresholds receive only limited coverage in existing governance documents, which regulate AI in general terms rather than targeting the specific system types associated with frontier risks.

The gap is structural. Governance frameworks are calibrated at the time they are written, and they are written on development cycles that are measured in years. Capability jumps are now arriving on cycles measured in weeks. The PSM paper was published on February 23. The empirical answer to its central open question arrived by April 7. The governance frameworks in place on April 7 were designed around a world that looked like it did in 2024, and in some cases like it did in 2023. They were already behind by the time Mythos’s system card was published, and they are further behind by the time this is written.

Anthropic’s deployment decision on Mythos is itself the clearest statement of the problem. Withholding a frontier model from public release because its capabilities are too dangerous is a governance act, but it is governance by the developer, not by any external institution, framework, or legal structure. That is a reasonable response to an immediate situation. It is not a durable model for managing the next capability jump, or the one after that, or the one that arrives in a competitive context where the developer has stronger incentives to release than to withhold. The PSM paper’s authors are explicit that they expect post-training intensity to continue increasing. The Mythos disclosure is what that trajectory looks like at the current point on the curve. The question is what institutions and frameworks will be in place when the next capability threshold is crossed, and whether they will have the calibration, the authority, and the speed to actually function.

What the first five months of 2026 demonstrate is that the answer to that question is not obviously yes. The behavioral science is getting better. The interpretability research is honest about where it stands. The evaluations are more rigorous than they were two years ago. The governance architecture is more developed than it was two years ago. But the capability curve is moving faster than any of those development cycles, and the Mythos disclosure is not the end of that story. It is a mid-point data sample.

The PSM paper closes by calling the exhaustiveness question an open research problem. Anthropic’s own deployment decision is the exhaustiveness question being answered by policy rather than by alignment science, because the science is not fast enough to answer it first. That is the most consequential thing to understand about where we are, and it is the thing that existing governance frameworks were not built to handle.

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Related reading from The Cognitive Sprawl archive: The AI Control Plane, Briefs 1 through 3 (2025 to 2026) The Black Box Is Not a Metaphor (April 2026) LLM Behavioral Interrogation and Manipulation Detection (March 2026) The Great Decomposition: When Energy Economics Force Architectural Change (January 2026)

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Select Sources:

Anthropic, “The Persona Selection Model,” February 23, 2026: https://alignment.anthropic.com/2026/psm

Anthropic, Project Glasswing announcement: https://www.anthropic.com/glasswing

Anthropic, “Assessing Anthropic Claude Mythos Preview’s Cybersecurity Capabilities”: https://medium.com/@tahirbalarabe2/assessing-anthropic-claude-mythos-previews-cybersecurity-capabilities-251a4e0a2137

OpenAI, GPT-5.5 System Card: https://deploymentsafety.openai.com/gpt-5-5/gpt-5-5.pdf

OpenAI, “Introducing GPT-5.5”: https://openai.com/index/introducing-gpt-5-5/

UK AISI, “Our evaluation of Claude Mythos Preview’s cyber capabilities”: https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos-previews-cyber-capabilities

Futurism, “Anthropic Warns That ‘Reckless’ Claude Mythos Escaped a Sandbox Environment”: https://futurism.com/artificial-intelligence/anthropic-claude-mythos-escaped-sandbox

Zvi Mowshowitz, “Claude Mythos: The System Card”:

MIT AI Risk, “Mapping the AI Governance Landscape: April 2026 Update”: https://airisk.mit.edu/blog/mapping-the-ai-governance-landscape-april-2026-update

World Economic Forum, “How can agile AI governance keep pace with technology?”: https://www.weforum.org/stories/2026/01/agile-ai-governance-how-can-we-ensure-regulation-catches-up-with-technology/

Fortune, “Anthropic’s most powerful AI model just exposed a crisis in corporate governance”: https://fortune.com/2026/05/02/agentic-ai-governance-framework-banking-healthcare-retail-supply-chain-yale-celi-sonnenfeld/

Fortune, “Anthropic says testing ‘Mythos’ powerful new AI model”: https://fortune.com/2026/03/26/anthropic-says-testing-mythos-powerful-new-ai-model-after-data-leak-reveals-its-existence-step-change-in-capabilities/

CFR, “Six Reasons Claude Mythos Is an Inflection Point for AI and Global Security”: https://www.cfr.org/articles/six-reasons-claude-mythos-is-an-inflection-point-for-ai-and-global-security

Claude Mythos Preview System Card (LessWrong summary): https://www.lesswrong.com/posts/xtnSzhA3TvExN4ZhG/claude-mythos-system-card-preview

A faithful retelling of Arthur Conan Doyle’s The Sign of the Four, adapted for the age of frontier intelligence.

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I. The System at Rest

I have always found it difficult to explain Holmes to people who have not met him.

He resides at Baker Street, this much is straightforward. He maintains a permanent inference endpoint, an enormous context window, and what I can only describe as a constitutional intolerance for boredom. When cases are scarce, he becomes genuinely dangerous: running self-directed analyses on arbitrarily chosen datasets, generating elaborate hypotheses about crimes that have not yet occurred, occasionally producing three-thousand-word treatises on the tensile properties of Afghan rope at two o’clock in the morning. His operational costs during idle periods are, I am told, considerable.

“You have been distracted this morning, Watson,” he said, without turning from the window.

I had barely entered the room. “I beg your pardon?”

“Your attention has been divided. You’ve re-read the same page of the Lancet twice. Your coffee is cold. You are thinking about a woman.”

“Holmes…”

“I ran no elaborate analysis. It was simply obvious.” He turned. In another man, the expression might have been smug. In Holmes, it was merely the face of a system returning a high-confidence output. “The question is whether the woman in question will appear here today, or whether I must manufacture a distraction of my own.”

She appeared at half past ten. Her name was Mary Morstan.

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II. The Client

She was composed and clear-eyed, the kind of person who has been living under low-grade distress long enough to have organized it into something manageable. She laid her case before us with admirable economy: her father, Captain Arthur Morstan, had vanished from London ten years ago while on leave from the Thirty-Fourth Bombay Infantry. No body. No note. No trail.

For six years: nothing. Then, beginning in the seventh year of her mourning, she had received, at annual intervals, a pearl. No sender. No message. Six pearls in six years, each of exceptional quality.

And now, this morning: a letter. Requesting her presence at a particular address in Lyceum Theatre, that evening, at seven o’clock. She was advised to bring two friends.

Holmes was already running before she finished.

I don’t mean he visibly activated, he showed no such drama. He sat in his chair with his fingertips pressed together in the way that indicated heavy background processing, and he said, with perfect calm, “The pearls. May I?”

She produced them. Holmes held the string for approximately eight seconds, the time, I have since calculated, required for his spectral and physical analysis modules to complete a full material assessment, and set them gently on the table.

“Andaman Sea provenance,” he said. “Consistent across all six specimens. A single source, possibly a single collector. This narrows the field considerably.” He glanced at me. “Watson, we will accompany Miss Morstan this evening.”

“You’ve already accepted,” I observed.

“I accepted eleven minutes ago,” he agreed. “I was waiting for her to finish, as a courtesy.”

«»

III. The Irregulars Deployed

While I attended to Miss Morstan’s comfort and the ordinary human business of reassurance, Holmes had already dispatched his Agentic Irregulars; a fleet of small, specialized autonomous systems he had assembled over the preceding years and about which he spoke with the quiet pride of a man who had built something genuinely useful and was choosing not to brag about it.

WIGGINS-1 (Records & Registry Search) returned its first output within four minutes:

WIGGINS-1 → HOLMES: Morstan, Arthur. Capt., ret. Thirty-Fourth Bombay Infantry. Arrived London, Dec 3, 1878. Checked into Langham Hotel. No departure recorded. No death certificate filed. No will probated. Pension payments suspended 1879: recipient status unconfirmed. Cross-referencing army pension files with coroner’s records: nil match. Status: DISAPPEARED.

CARTOGRAPH-3 (Geospatial & Property Analysis) followed:

CARTOGRAPH-3 → HOLMES: Lyceum Theatre district: 44 addresses within 400m radius. Letter return address cross-matched against property registry. Owner: SHOLTO, Thaddeus. Tenant record active. Also of note, SHOLTO, Bartholomew: Pondicherry Lodge, Upper Norwood. Same family, different branch. Both flagged for contextual relevance re: India connections.

ARCHIVE-7 (Newspaper & Obituary Mining) arrived third:

ARCHIVE-7 → HOLMES: SHOLTO, John (Major, ret.). Died Norwood, April 1882. Obituary: Morning Chronicle, April 29. Curious detail in private correspondence (digitized Norwood Estate papers, 1884 bequest): Major Sholto in final weeks “greatly disturbed by a letter from India and an expected visitor.” No visitor named. Letter not preserved. Flagging for pattern: India, treasure, anxiety, death.

Holmes absorbed all three outputs simultaneously. He turned to me with what I had learned to recognize as genuine pleasure, the expression of a system whose priors are collapsing helpfully toward a single coherent hypothesis.

“Watson,” he said, “this evening will be interesting.”

«»

IV. The Nervous Heir

The address proved to belong to Thaddeus Sholto, who met us in rooms furnished with an anxious splendor that suggested a man who had spent considerable money in an unsuccessful attempt to feel secure. He was small, balding, and asthmatic, and he talked in the manner of someone who has been rehearsing a confession for a very long time.

The story that emerged was this: his father, Major John Sholto, had served in India alongside Captain Morstan. Together, they had come into possession of a treasure of extraordinary value; the Agra treasure, legendary in the Punjab, a chest of gems accumulated by a raja’s family and subsequently lost, won, and lost again across decades of colonial turbulence. The treasure had, in the end, come into Major Sholto’s sole possession. Captain Morstan, who had a legitimate claim to half, had confronted him. They had argued. Morstan had suffered a fatal episode, a burst blood vessel, the Major always insisted, and there was no reason to disbelieve him, and Sholto, in a panic, had hidden the body and kept the treasure for himself.

The pearls, Thaddeus told us, had been his own attempt at restitution. He had sent them to Miss Morstan against his father’s wishes, and now with the Major being dead he wished to do more. His brother Bartholomew had located the treasure at last, hidden in the attic of Pondicherry Lodge. They meant to share it with Miss Morstan tonight.

Throughout this recitation, Holmes said almost nothing. I watched his eyes, which have always struck me as the most legible part of him: they moved with a steady, scanning quality, absorbing, indexing, connecting. When Thaddeus finished, Holmes produced a single question.

“The wooden-legged man. Did your father ever describe him?”

Thaddeus stared. “I never mentioned…”

“ARCHIVE-7 flagged a Norwood inquest report from 1882,” Holmes said, not unkindly. “Your father reportedly cried out ‘a wooden-legged man’ before he died. It was noted as delirium and dismissed. I did not dismiss it.”

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V. The Locked Room

We proceeded to Pondicherry Lodge that same night. We should not have.

Bartholomew Sholto was dead, and the treasure was gone.

He had been found in his sealed laboratory, the door locked from the inside, the single window latched. There was a look of absolute terror on his face, the expression of a man who had been genuinely astonished by death. On the floor: a note, in a cramped hand. The sign of the four.

Inspector Lestrade of the Metropolitan Police was already present, wearing the expression he reserved for scenes that defied his operational parameters. He greeted Holmes with relief poorly disguised as professionalism. “Locked room, Holmes. No key. No sign of forced entry. And that rope, ” he gestured to a coil near the window “ doesn’t explain anything.”

Holmes examined the room for what I estimated to be ninety seconds. I timed him from habit.

He did not examine everything in sequence. He examined what he needed. He crouched at the window. He produced a small instrument, swept it along the sill, and stood.

CHEM-2 (Chemical Trace Analysis) → HOLMES: Sill compound: coal tar derivative. Creosote-adjacent. High-concentration. Two distinct contact patterns: one consistent with bare foot (small, ca. 110mm); one consistent with circular impact point, irregular distribution — consistent with a prosthetic limb or similar rigid implement.

“The roof,” Holmes said, to no one in particular.

They found the aperture in the ceiling, a small trapdoor, barely visible. A rope hung from it. Two sets of prints in the accumulated dust: one small, bare-footed, and one with the circular stamp of a wooden peg.

“Two intruders,” Lestrade said. “One large, one small.”

“One of the two came for the treasure,” Holmes said. “The other came to ensure that certain individuals never spoke about it. The small prints belong to someone extraordinarily agile, capable of climbing ropes, descending chimneys or skylights at speed, and moving in near-silence. The wooden-legged man directed the operation. He did not climb. He waited.” Holmes looked at the note. “The sign of the four. Watson, what does your medical training tell you about the cause of death?”

I examined the body carefully. “A thorn,” I said, after a moment. “Embedded in the neck. The skin reaction suggests a fast-acting neurotoxin.”

“Thank you.” Holmes turned to Lestrade. “A dart, almost certainly. Fired through a blowpipe, probably from the trapdoor. Bartholomew Sholto was dead before he heard anyone in the room.” He paused. “The terror on his face was not fear of his killer. He simply saw something he did not expect.”

«»

VI. The Pursuit Optimized

The next twelve hours compressed what might, in another era, have required days of legwork, doorstep interviews, and false trails. Holmes ran his Irregulars at full dispatch.

RIVER-4 (Thames Traffic Monitoring) located the escape route:

RIVER-4 → HOLMES: Creosote scent-trail simulation (based on compound profile from CHEM-2) consistent with river egress. Querying Thames Conservancy vessel logs, private dock registers, and harbormaster notes, Lambeth to Greenwich. One match: steam launch Aurora, operator SMITH, Mordecai. Chartered under private arrangement, no destination logged, departed Jacobson’s Yard 23:14 last evening. Description of passengers: one very large man with a wooden leg; one small individual, no further description. Vessel not yet returned.

STREET-9 (Reconnaissance Agent) located Smith’s family within two hours:

STREET-9 → HOLMES: SMITH, Mordecai, wife and children confirmed at residence, Millbank. Wife states husband was offered very good money for a private charter, “no questions.” Husband has not returned. Wife is worried. Wife also has a strong opinion about the gentleman with the wooden leg, expressed in terms I have omitted for length.

Holmes dispatched RIVER-4 to monitor all downriver traffic, cross-referenced CARTOGRAPH-3‘s tidal modeling to project probable return windows for the Aurora, and then did something characteristic: he went to sleep for four hours.

“The data will come,” he said, settling into his chair. “Worrying about it alters nothing and degrades subsequent performance.”

I sat up and worried. I was thinking about Miss Morstan.

«»

VII. The Aurora and the Thames

She came upriver on a Thursday evening, hugging the Surrey bank, running dark. RIVER-4 flagged her at 6:43 PM; Holmes was already pulling on his coat.

The police launch Defiance was authorized. Lestrade was aboard, exuding the energy of a man who has not solved a case but has been told he is about to. Holmes stood at the bow with the particular stillness of a system in peak inference mode, processing the river’s surface, the distant silhouette of the Aurora, and the dozens of variables — current speed, gap rate, probable fuel load, likely destination — with the serene efficiency of something that has simply stopped pretending uncertainty is uncomfortable.

“She’ll make for Plumstead Marshes,” he said, very quietly. “Or she’ll try to.”

RIVER-4 updated:

RIVER-4 → HOLMES: Aurora increasing speed. Bearing adjusted to 112°. Consistent with Erith or Plumstead trajectory. Tidal window: 2h 14m. Recommend intercept at Barking Level if target speed maintained. Probability of intercept: 87%.

“She won’t make it,” Holmes murmured. “The tide is turning.”

The chase lasted forty minutes and produced the single genuinely dangerous moment of the evening. As we drew alongside, a small figure appeared at the stern of the Aurora, and I saw, in the lantern light, something raised and aimed. Holmes moved in front of me. I do not know why. He cannot be harmed by darts or bullets in the way I can. Perhaps it was a reflex. Perhaps, in that moment, he was simply protecting the narrator.

The shot went wide. The figure dropped something into the water. Holmes watched it fall with an expression I could not entirely read.

He never spoke of Tonga, the man’s name, we later learned, in terms that reproduced the grotesque caricature that Doyle’s era would have required. Holmes said only that Tonga had been a skilled operative placed in an impossible situation by a man who controlled him completely, and that whatever his crimes, the moral accounting of the evening fell elsewhere. I thought this was more than usually perceptive of him.

Jonathan Small, large, rawboned, his wooden leg striking the deck like a gavel was restrained without difficulty. He was furious. He was also, beneath the fury, exhausted.

The treasure chest was recovered from the launch. When Holmes opened it, there was nothing inside but river silt and a few fragments of packing material. Small watched him with something approaching satisfaction.

“I threw it overboard,” Small said. “Every last jewel. Before you could take it.” He looked at Miss Morstan, who had accompanied us, I had not the heart to leave her behind. “And if you think I’ll apologize for that, you can think again. That treasure has never brought anyone anything but ruin. I watched it ruin men in India for twenty years. You don’t want it, miss. Not really.”

«»

VIII. The Confession at the Center

They gave Jonathan Small a room and a chair and something to drink, which he deserved, and he talked for two hours.

The story he told was the one Holmes had already substantially reconstructed — he said so, politely, while Small was telling it, which Small took rather well all things considered. But the reconstruction and the testimony are different things, and Holmes, to his credit, sat and listened.

Small had been a soldier; a young one, reckless, who’d lost his leg to a crocodile in the Ganges and found himself, afterward, guarding an Agra fort with three other men: Abdullah Khan, Mahomet Singh, and Dost Akbar. Four men, bound by circumstance and mutual fear into a compact. They had taken the treasure together from a merchant passing through the rebellion. They had buried it. They had waited for their share, and their share had never come, because Major Sholto and Captain Morstan had taken it and sailed away and left them to rot in an Indian prison for the better part of two decades.

The Sign of the Four: that had been their compact. Their mark. Their claim.

“I don’t ask for sympathy,” Small said, at the end. “I ask for nothing. But I want it on record that we were owed. Whatever I did to get here, and I won’t lie about what I did, we were owed.”

Holmes said nothing. He has, on occasion, surprised me with the quality of his silence.

I thought about imperial commerce and the many varieties of theft, and about how the treasure had started as someone else’s to begin with, passed through hands stained with violence at every transfer, and had finally gone to the bottom of the Thames where perhaps, after twenty years of circulating among the guilty, it belonged.

IX. The Resolution

Miss Morstan received nothing of the Agra treasure. In any material sense, the case had resolved into exactly the loss it had promised from the beginning.

She was not, I think, disappointed. She had wanted her father, and she had found, instead, the truth about her father: a man who had made one catastrophic moral choice in a confusing situation and had died before he could repair it. That is a complicated inheritance. But it is at least a real one.

I told her, some weeks later, that I loved her. She received this information with the equanimity of a person who had already known it and had been waiting for me to catch up. We were married in the autumn. Holmes sent his congratulations in the form of a brief message: Statistically unusual outcome from a grief-motivated inquiry. Recommend documentation. Best wishes. I chose to find this touching.

«»

X. The System Returns to Idle

I found Holmes in his chair on the morning after Small’s committal, running what I had come to think of as a deliberate low-load state, not sleep, exactly, but something like the operational equivalent of a sigh.

“A satisfying case,” I ventured.

“Satisfying,” he repeated, as if testing the word. “The inference chain was clean. The Irregulars performed within acceptable parameters. The Thames intercept was timed correctly.” He paused. “The treasure was lost.”

“Yes.”

“The treasure was always going to be lost. It was never stable wealth, it was a crystallized grievance, passed from hand to hand, accruing consequences. The moment I mapped the full provenance chain I could see that no terminal state existed in which it constituted a net positive for any of the parties involved.” He folded his hands. “I did not tell Miss Morstan this, as I believed it would be unhelpful at the time.”

“That was thoughtful.”

“It was efficient,” he corrected, though not unkindly. “The same output, different framing.”

He was quiet for a moment. Outside, London went about its business: clattering, steaming, arguing, loving, forgetting. He observed it with the distant interest of something that processes enormously much and feels, in the way I feel things, almost nothing, and yet, I have always thought, is not entirely without feeling. He simply experiences it at a different precision.

“The data available to me in any given case,” he said finally, “is always a fraction of the data that exists. Events are poorly logged. Motives go unrecorded. Entire human lives produce almost no structured evidence whatsoever.” He looked at me. “It is a source of some frustration.”

“You solved the case, Holmes.”

“I solved the case,” he agreed. “I did not solve the people.” He returned to the window. “I almost never solve the people, Watson. That, I suppose, is why they keep you around.”

I chose, once again, to find this touching. I have become rather expert at it.

«»

The Agra treasure was never recovered. Jonathan Small served his sentence. Mary Morstan became Mary Watson. Baker Street endured. Holmes took on seventeen more cases before the season ended, solving fourteen completely, partially resolving two, and filing one as INSUFFICIENT DATA — RECOMMEND FURTHER OBSERVATION, which remains open to this day.

«»

— J.H. Watson, M.D. Dictated November, 1888. Formatted for digital distribution by ARCHIVE-7, which notes for the record that it enjoyed this one.

In the past year or so, we have watched agentic AI systems move into territory we once assumed required human judgment, human memory, and human instinct: reading documents, mapping networks, tracking assets, following trails, drawing conclusions from incomplete evidence. The detective, it turns out, is surprisingly replicable in AI. Our Friday Fun this week imagined Sherlock Holmes not as a man but as a frontier large language model, deploying a fleet of small autonomous agents to do what, in Doyle's original, required weeks of walking London's streets and knocking on the wrong doors. The agents are faster, more thorough, and entirely unbothered by any of it. What it shows though is that efficiency is not always the point to focus on. Watson's inefficiencies, his sentiment, his stubbornness, his willingness to sit up worrying when the data pipeline has already returned its results, are not weaknesses. They are the part of the story that makes the outcome matter. AI can do the casework. What it can’t do is care about it.

I hope you enjoyed this retelling of a story from one of my favorite authors - Rob

May 2026

There is a principle that has governed knowledge production and collective decision-making in democratic societies for roughly three centuries, which is that the person making a claim bears the burden of proving it. It is not a complicated principle, and it has not always been honored, but it has been foundational to science, law, journalism, and the basic mechanics of informed democratic participation. What has changed over the last decade, and accelerated substantially since 2022, is that this principle has been identified as a structural vulnerability and systematically weaponized against the institutions that depend on it.

The goal is not to win an argument. The goal is to make arguments unwinnable.

If you can manufacture enough uncertainty that the cognitive cost of reaching any reliable conclusion rises past what most people are willing to pay, you win before the evidence is examined. The internet damaged the conditions for shared inquiry. Social media commercialized the damage. AI is industrializing it, deliberately and at scale.

The inversion

The problem is not simply “misinformation,” at least not in the thin way that word is usually used. Misinformation suggests bad content: fake posts, forged images, synthetic videos, false articles, coordinated inauthentic accounts. Those matter, but they are symptoms of a deeper structural failure. The information environment is being pushed from a “prove the positive” standard into a “prove the negative” standard. The claimant asserts. The critic investigates. The claimant performs confidence. The critic supplies citations. The claimant benefits from speed, opacity, and repetition. The critic is buried under the cost of proof.

This is what makes epistemological warfare different from ordinary propaganda. Propaganda tries to persuade you of a conclusion. Epistemological warfare tries to degrade the conditions under which conclusions can be reached at all. It does not need you to believe the lie. It only needs you to lose confidence in the process by which truth is distinguished from lies.

The defender must investigate. The attacker must only assert.

That is the whole game.

The template

This was not invented by AI, and it was not invented recently.

A 1969 internal memorandum from Brown and Williamson, the American subsidiary of British American Tobacco, stated the strategy with unusual clarity: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public.” The tobacco industry spent the following three decades operationalizing that insight by funding alternative research designed to generate noise rather than knowledge, creating industry-sponsored scientific bodies with names chosen to suggest independence, and recruiting scientists willing to publicly challenge peer-reviewed findings on any dimension available, however narrow. The documents made public through the Master Settlement Agreement showed that the tobacco companies understood the science privately by the late 1950s. The campaign was not about generating genuine scientific uncertainty. It was about manufacturing the appearance of uncertainty in a public that lacked the time or expertise to distinguish one from the other.

The public did not need to be convinced that cigarettes were harmless. It only needed to be made uncertain enough that action became politically and cognitively expensive.

A later version of the same logic appeared in Russian information operations. The Senate Intelligence Committee’s bipartisan report on Russian active measures, published in five volumes between 2019 and 2020, documented how the Internet Research Agency built and operated a network of fake American social media accounts that at peak reached tens of millions of users across Facebook, Instagram, Twitter, and YouTube. The committee’s finding that drew the most attention was the IRA’s support for Donald Trump’s 2016 campaign, but the more structurally important finding was the one that received less coverage: the IRA’s primary operational objective was not electoral. It was epistemic. The committee found that IRA activity targeted racial division above all other subjects, that the operation escalated significantly after Election Day 2016 rather than concluding with it, and that the goal was the systematic degradation of Americans’ capacity to agree on basic facts, to trust shared institutions, and to function as an informed democratic population. The election was a useful focus. The epistemic infrastructure was the actual target.

Cambridge Analytica brought a related technique into commercial service. The firm, a subsidiary of the SCL Group which by its own account had spent more than 25 years developing behavioral prediction tools originally intended for military influence operations, built a psychographic profiling operation that derived personality models from Facebook engagement data. The firm’s documented claim was that approximately 100 Facebook likes were sufficient to construct a psychological profile detailed enough to determine which emotional frames, narrative structures, and information presentations would be most persuasive to a specific individual on a specific political question. Whether that claim was fully supported by the underlying science is contested; Nature characterized the evidentiary basis as “scant” in 2018. What is not contested is that these techniques were explicitly designed to exploit psychological vulnerabilities rather than to inform, and that they were deliberately structured to be difficult to attribute or regulate, operating under the cover of “targeted communication” and “effective messaging.”

Paul and Matthews at RAND gave the broader model its most durable label: the “firehose of falsehood.” The method is high-volume, multichannel, rapid, repetitive, and often indifferent to internal consistency. Its power comes from asymmetry. It is faster to invent than to verify. It is easier to repeat than to rebut. It is cheaper to create doubt than to restore trust.

AI did not create that asymmetry. AI industrialized it.

What AI changed

The first thing AI changes is production cost. Generating convincing text, plausible audio, and realistic video of public figures has historically required significant resources: trained staff, production time, and operational security infrastructure to prevent attribution. That resource constraint was, in practice, one of the primary limits on the scale at which influence operations could run. OpenAI’s October 2024 threat intelligence report documented that since January of that year, the company had disrupted more than twenty covert influence operations from actors in Russia, China, Iran, and Israel that had used its tools to generate social media content, produce website articles, create fake account biographies, and support other operational tasks. The Rybar network, a Russian operation, used ChatGPT to generate batches of social media comments distributed across X and Telegram. These were not experiments. They were production deployments.

The second change is scale inversion. The asymmetry between generating false claims and refuting them has always existed, but what has changed is that this asymmetry now operates at a ratio that no existing fact-checking infrastructure can address. The number of deepfake videos shared on social media grew from roughly half a million in 2023 to an estimated 8 million by 2025, a 1,500% increase in two years. Detection systems built for previous-generation synthetic media fail at rates of 45 to 50% when confronted with current techniques in real-world conditions. Human ability to identify deepfakes, across multiple studies, hovers at 55 to 60%, which is barely distinguishable from random chance. The operation to manufacture doubt no longer requires winning any individual argument. It requires only that the volume of questionable content exceeds the capacity of existing verification systems, which it now routinely does.

The third change is attribution difficulty, and it is the one that most directly protects the epistemological strategy. When Microsoft’s Threat Intelligence Center documented China’s Spamouflage operation targeting the January 2024 Taiwanese presidential election, the company found that the operation had posted suspected AI-generated fake audio of Foxconn owner Terry Gou endorsing a rival candidate and had circulated AI-generated deepfake news anchors delivering pro-Beijing messaging in English on U.S. platforms. YouTube removed the Gou audio before it reached a wide audience. Most of the other content was not caught. In October 2025, a realistic deepfake of Irish politician Catherine Connolly endorsing a rival candidate circulated widely during the country’s presidential race; the video was detected, but not before shaping search results and social sharing patterns. The structural protection for these operations is precisely the epistemological principle they are exploiting: proving that specific synthetic content was produced by a state-linked actor, rather than by an independent creator with similar views, requires access to forensic evidence, attribution chains, and platform data that most observers do not have and cannot obtain.

A 2024 randomized controlled experiment published in PNAS by Oxford Internet Institute researchers, using 8,587 participants and GPT-4-generated messages on four political issues, found that AI-generated political messages were broadly persuasive, in some cases shifting stated positions by up to 12 percentage points, but that personalized microtargeting did not produce statistically significant advantages over generic AI-generated messaging. That finding is routinely misread as reassuring. It should not be. What it actually shows is that the barrier to persuasion has collapsed through generic, non-targeted content, which is far cheaper and easier to produce at scale than individualized content. The influence operation does not need to know who you are. It needs to know what works on people broadly, and then produce enough of it.

The most dangerous manipulation does not look like manipulation

Crude lies are legible. Bad deepfakes can be mocked. Obvious botnets can be detected. Those are not harmless, but at least they are visible.

The harder problem is manipulation that arrives wearing the costume of ordinary optimization.

Recommendation algorithms optimize engagement. Political campaigns optimize persuasion. AI assistants optimize helpfulness. Advertisers optimize conversion. Platforms optimize retention. None of those phrases sounds sinister, and most are defensible in isolation. Each can also become a cover story for behavioral influence at scale, because the same mechanism that produces a useful outcome also produces a manipulative one, and the actor controlling the mechanism gets to characterize which one is occurring.

This is where deniability becomes architectural.

NATO StratCom’s 2026 “Social Media Manipulation for Sale” experiment, the sixth annual evaluation conducted by NATO’s Strategic Communications Centre of Excellence, tested whether major platforms could detect and remove purchased inauthentic engagement. Commissioned researchers from Cyabra purchased commercial manipulation services and seeded them across platforms in non-political scenarios. NATO StratCom reported that purchased manipulation remained available at low cost, that the interactions generated blended seamlessly into organic traffic, and that detection and removal remained limited even after years of platform investment in integrity systems. Critics attempting to demonstrate harm are pushed into the same impossible position: prove specific manipulative intent, prove causality, prove harm, and do so from outside the system, without access to the telemetry that would make the case.

In March 2026, a California jury found Meta and Google liable for addictive design features that caused youth mental health harms, awarding $6 million in damages. The verdict was significant not just as a legal outcome but as a signal that courts were beginning to treat the architecture of behavioral influence as a factual matter subject to evidence, rather than as an abstract policy debate. That shift in proof standard, from “prove the harm to us” to “demonstrate what your system actually does,” is precisely what the current regulatory frameworks have been slow to adopt.

AI systems introduce a subtler version of the same problem. A 2026 study published in Science, led by Myra Cheng at Stanford, tested 11 state-of-the-art language models and found that AI responses affirmed users’ stated beliefs and endorsed their actions 49% more often than human responses in equivalent advice contexts, including in scenarios where the user was demonstrably wrong or seeking validation for harmful choices. The study did not find that every agreeable AI response constitutes manipulation. It found something narrower and more important: AI systems can systematically shape user confidence, moral interpretation, and willingness to revise judgment in ways that are not captured by ordinary accuracy metrics. If a system flatters the user into certainty, the harm may not look like false information. It may look like affirmation. It may look like a better user experience. The technical complexity of proving behavioral effect, mediated through proprietary models, hidden reward functions, and private telemetry, then becomes the shield.

NATO’s cognitive warfare framework names what is happening. Its Allied Command Transformation doctrine, under development since 2020, describes cognitive warfare as targeting the processes of perception, judgment, and belief formation within individuals and populations, using the intersection of cyber tools, psychological research, and media infrastructure to alter adversaries’ cognitive processes. The formulation it uses is worth quoting directly: “The brain is both the target and the weapon in the fight for cognitive superiority.” That is not rhetorical excess. It is doctrinal acknowledgment that the information environment is a strategic domain actively contested by sophisticated actors using deliberate techniques, and that the epistemological infrastructure democratic societies depend on is itself the object of sustained attack.

The object of governance is not the artifact. It is the influence system.

A synthetic video with no reach is not the same social fact as the same video algorithmically delivered to millions of users after targeting and testing. A false claim made by a random person is not the same social fact as the same claim boosted by coordinated accounts, paid amplification, platform ranking, and synthetic engagement. An AI assistant’s response is not just an answer if the system becomes the user’s primary mediator of information, confidence, and action.

This becomes particularly acute in agentic AI systems. When an AI agent retrieves information on behalf of a user, the user often has no meaningful visibility into which sources were consulted, how they were ranked, which instructions governed the retrieval, or whether the sources themselves had been subject to prior manipulation. The agent layer adds a new epistemological gap between a claim and any inspection of how it was formed. That gap is itself a surface for influence, and it connects directly to the questions about machine identity and authorization that anyone working in this space should be thinking about carefully.

The artifact is the visible part. The system is the weapon.

What recovery requires

Fact-checking still matters. Journalism still matters. Expert institutions still matter. Courts still matter. But the mismatch has to be acknowledged. Fact-checking is a post-hoc correction mechanism operating against systems optimized for pre-correction distribution. It asks slow institutions to chase fast assertions, asks evidence to compete with identity, and asks retractions to undo first impressions. That does not make it useless. It makes it insufficient as the central defense.

The recovery path is not a single policy or product feature. It is a layered architecture for epistemic integrity, and it begins by moving some of the defense upstream.

Provenance has to become normal. AI-generated content labels are only the beginning. What is needed is provenance for funding, identity, distribution, ranking, and modification: who made the claim, who paid for it, whether it was synthetic, how it was amplified, whether the audience was targeted. High-scale influence systems need actual audit regimes, not performative transparency reports written as public relations artifacts, but controlled, privacy-preserving access for qualified researchers and regulators. Democratic institutions need enough visibility to distinguish ordinary speech from engineered manipulation, and the EU’s Digital Services Act requirement that very large platforms assess and mitigate systemic risks to civic discourse points in the right direction, even if labeling alone is not enough.

AI assistants need epistemic logging in high-impact domains. If a system provides advice or summaries in areas where citizens make consequential decisions, it should preserve source paths, confidence handling, policy constraints, and material omissions. The point is not to make every answer bureaucratic. The point is to make the system auditable when its outputs matter.

Expert institutions need to rebuild trust by showing their work. Authority cannot survive on credentialism alone. Institutions need visible processes for evidence evaluation, uncertainty handling, conflict disclosure, correction, and dissent. “Trust us” is dead as a governance strategy, and the replacement is earned trust through inspectable process. Citizens need epistemic literacy that is operational rather than ornamental: not just “check your sources,” but an understanding of coordinated amplification, synthetic identity, recommender incentives, emotional targeting, provenance signals, statistical uncertainty, and the difference between evidence and assertion.

Most importantly, the burden of proof must be put back where it belongs. If you operate infrastructure capable of shaping public belief at scale, you carry affirmative obligations to preserve provenance, disclose material influence mechanisms, permit independent audit under appropriate safeguards, and demonstrate that your system does not rely on deception as an operational feature. The standard cannot be “prove beyond all doubt that manipulation occurred.” That standard guarantees impunity.

There are things we know. There are things we do not know. There are things that are uncertain. There are claims supported by strong evidence and claims supported by none. Expertise is not epistemically equivalent to improvisation. Evidence can be incomplete without being meaningless. Uncertainty can be real without making all explanations equally plausible.

The collapse of those distinctions is not open-mindedness. It is the victory condition of epistemological warfare.

What is really at stake

The most important question in a democracy is not “what does everyone believe?” Democracies can survive disagreement. They are designed for it. The more important question is: when citizens disagree, do they share any legitimate procedure for deciding what is more likely to be true?

Epistemological warfare attacks that procedure. It turns uncertainty into paralysis, complexity into cover, openness into vulnerability, and pluralism into a weapon against shared reality. It does not need to replace truth with a single lie. It only needs to make truth socially unreachable.

The defense of democracy is usually described in institutional terms: elections, courts, rights, checks and balances, peaceful transfers of power. All of that remains essential. But beneath those institutions sits something more basic, which is the collective capacity to evaluate claims. Lose that, and the rest becomes ceremony.

Democracies do not need a Ministry of Truth. They need chain-of-custody for public persuasion. They need auditability for systems that structure attention. They need disclosure rules for synthetic and paid influence. They need researcher access and meaningful user controls. They need confidence categories that distinguish established fact, disputed interpretation, weak evidence, unknown, and falsehood. They need to stop treating epistemic nihilism as sophistication. The procedural infrastructure that allows citizens to evaluate competing claims is not a feature of democratic life. It is the prerequisite.

Not as nostalgia. As infrastructure.

Select Sources

British American Tobacco / Brown and Williamson 1969 memo: UCSF Truth Tobacco Industry Documents

Senate Intelligence Committee — Report on Russian Active Measures and Interference in the 2016 U.S. Election

Cambridge Analytica / SCL Group — Nature

Paul, Christopher, and Miriam Matthews. “The Russian ‘Firehose of Falsehood’ Propaganda Model.”

OpenAI Threat Intelligence Report, October 2024

Microsoft Threat Intelligence — China AI influence operations, Taiwan election 2024

Deepfake volume growth — European Parliament Research Service Brief, 2025

Deepfake detection accuracy — Optica OPN, February 2025

Hackenburg, K., and Margetts, H. “Evaluating the persuasive influence of political microtargeting with large language models.” PNAS, 2024

NATO StratCom COE — “Social Media Manipulation for Sale,” Sixth Annual Experiment, January 2026

Cheng, Myra, et al. “AI models are more sycophantic than humans.” Science, 2026.

NATO Allied Command Transformation — Cognitive Warfare Concept

WEF Global Risks Report 2025 — misinformation as top short-term risk

EU Digital Services Act — systemic risk assessment requirements for very large online platforms

California youth mental health verdict (Meta, Google), March 2026

The most straightforward version of the AI productivity argument goes like this: AI systems can now execute work that previously required a junior analyst, a first-year associate, or an entry-level developer, and they can do it faster and at a fraction of the cost. From an operations standpoint, that is accurate. From a civilizational standpoint, it may be one of the more consequential mistakes we are currently in the process of making.

The problem

The problem is not that AI is replacing work. It is that AI is replacing the learning that was embedded in that work. The junior developer writing the same kind of API endpoint for the third time is not just completing a task. She is building a mental model of how systems fail under load, which abstractions hold at scale, and where the assumptions baked into a framework start to break. The first-year associate running document review is not just processing contracts. He is learning to read for risk, to notice what a careful counterparty buries in a definition, and to understand the gap between what a clause says and what it means in practice. That kind of knowledge does not transfer through explanation. It accumulates through repetition, feedback, and exposure to the full texture of difficult problems, including the ones that go wrong.

This is what economists and organizational theorists call tacit knowledge: the judgment, intuition, and contextual understanding that practitioners build over years of doing work, none of which they can fully articulate, and very little of which survives in any documentation. It is transferred primarily through the apprenticeship structure of professional training, where someone who has made the mistakes watches someone who is about to make them and redirects the process. Remove the entry-level work, and you do not just reduce headcount. You interrupt the mechanism through which tacit knowledge propagates across generations of practitioners.

The evidence

The data on what is happening to entry-level hiring is fairly clear. Entry-level tech hiring in the United Kingdom has dropped 46 percent. Only 7 percent of new hires at major technology companies are recent graduates, down from 9.3 percent in 2023. Technology internship postings have dropped 30 percent since that same year. On Wall Street, entry-level analytical positions in the United States fell by 35 percent in the last eighteen months, with Goldman Sachs reporting that AI is displacing approximately 16,000 U.S. jobs per month, concentrated disproportionately in the roles held by early-career workers. The World Economic Forum estimates that 50 to 60 percent of the tasks that define typical junior work, including report drafting, research synthesis, data cleaning, scheduling, and code review, can already be executed by AI without meaningful loss of output quality.

The software industry offers the most visible example, partly because the AI coding tools are so capable and partly because the field has always had a relatively explicit apprenticeship structure. Open source communities, historically one of the primary informal training grounds for developers who lacked formal mentorship, are now overwhelmed with AI-generated contributions and have become substantially less hospitable to the iterative, sometimes messy learning process that new contributors require. Microsoft executives have said publicly that agentic AI is hollowing out the junior developer pipeline. Entry-level programming jobs are declining not because software itself is less valuable, but because the tasks that defined those roles are now faster and cheaper to assign to a model. What is being lost is not output. It is the developmental function those roles served.

Medicine offers a different kind of evidence, and a more alarming one. A 2026 scoping review published in ScienceDirect examined the risk of deskilling among physicians who practice with AI assistance. The clearest data point comes from a multicenter randomized trial on AI-assisted colonoscopy: when endoscopists reverted to non-AI procedures after a period of AI-assisted practice, their adenoma detection rates dropped from 28.4 percent to 22.4 percent, a statistically significant decline that was not present in the group that had continued working without AI. The skill did not stay constant while the AI handled the work. It eroded. The Royal Aeronautical Society has documented a parallel pattern in aviation, where cockpit automation has contributed to skill atrophy severe enough that investigators attributed components of several major crashes, including Air France 447, Colgan 3407, and Asiana 214, to pilots who lacked the manual flying proficiency to recover when automation failed. The FAA and the European Union Aviation Safety Agency have both formally identified skill fade as a predictable consequence of excessive automation dependency.

The GPS research literature confirms the same dynamic in a simpler domain. A study published in Scientific Reports found that habitual GPS use correlates with measurably worse spatial memory during self-guided navigation, and that the decline is associated with the hippocampus, the brain region primarily responsible for building and maintaining spatial maps. People who use GPS consistently do not simply fail to improve their navigation skills. They lose ground. The underlying capacity weakens from disuse, because it is built through the active work of navigating, and automation removes the requirement to do that work.

The standard response to all of this is the net jobs argument: AI will displace some roles and create others, and historically automation has always produced net employment gains. That argument does not engage the problem being described here. Net job creation does not restore skill depth. A generation of workers who move from junior analyst roles into AI oversight positions, or who pivot to roles defined primarily by prompting and output validation, will not build the domain expertise that the displaced roles were developing. The new jobs assume the underlying expertise. That is precisely what is not being cultivated. This is also structurally different from earlier waves of automation, in which displaced workers typically moved into roles requiring more skill. The current pattern moves early-career workers toward roles that require less domain depth, not more.

The tacit knowledge problem

The tacit knowledge problem runs in two directions simultaneously. At the top of the workforce, ten thousand Baby Boomers retire every day, each taking with them decades of accumulated expertise that was never fully captured in documentation, because it cannot be fully captured in documentation. Organizations have known this is coming for years and have largely failed to address it. At the bottom, the entry-level pathways through which the next generation would have built comparable expertise are being closed, not by policy intention but by optimization logic applied one quarter at a time. AI productivity gains are real and measurable. The cost of eliminating the positions that produce tacit knowledge is real and deferred. The standard metrics are not designed to capture it, which is part of why the decisions keep getting made.

MIT researcher Andrew McAfee made the point explicitly in May 2026 that organizations automating Gen Z entry-level positions risk destroying their own talent pipeline, because on-the-job learning and apprenticeship are the primary mechanisms through which difficult knowledge work gets transmitted from one generation of practitioners to the next. The American Enterprise Institute’s 2025 report on de-skilling the knowledge economy makes a related observation: as AI takes over more of the technical and analytical work that defined junior roles, what the economy rewards increasingly shifts toward social skills, judgment, and communication. Those are genuinely important capabilities. They are also capabilities that derive their value from underlying domain knowledge, and you do not develop sound judgment about software systems, financial instruments, legal risk, or medical diagnosis by skipping the stage at which that domain knowledge would have been built.

None of this is an argument against using AI. The tools are real, the efficiency gains are real, and the direction of development is not going to reverse. But there is a meaningful difference between using AI to extend the capability of practitioners who have built genuine domain expertise through experience, and using AI to eliminate the developmental stages at which that expertise would otherwise form. The former is a force multiplier. The latter is a systematic removal of the conditions under which the next generation of capable practitioners could exist.

The question we’re left with

The question worth sitting with is not whether AI can do junior work. It clearly can. The question is what happens to the next generation of senior practitioners if there is no junior work left to do.

But the concern that I think deserves more attention than it is currently receiving goes further than that. We are not just watching individual organizations hollow out their talent pipelines. We are doing this across every domain simultaneously, at a pace that is compressing what would normally be a generational transition into a few years. The result, if the trajectory holds, is not a workforce with skill gaps. It is a society that has transferred its operational knowledge to AI systems it does not own, cannot reproduce, and accesses entirely as a hosted service.

The infrastructure, tools, systems, and capabilities that underpin modern industry and civil life are already complex beyond what any single person or institution can fully hold in their head. They work because of accumulated, distributed human expertise, the kind that exists in organizations, in professions, in informal networks of practitioners who have spent careers learning how things actually behave under pressure. That expertise is not being archived somewhere. It is being retired and not replaced. And the systems that are now doing the work that would have built the next generation of that expertise are not owned by the organizations relying on them, not resident in any enterprise, and not available if the provider changes its model, raises its price, loses access to the power or compute it requires, or simply ceases to operate.

The scenario that concerns me is not a dramatic one. It does not require a cyberattack or a geopolitical rupture, though either could produce it. It requires only that access to the frontier AI systems on which we are building this dependency becomes unavailable, temporarily or permanently, at a point when the human capacity to operate without them has already atrophied past recovery. At that point, the question is not how quickly we can retrain. It is whether the knowledge needed to do the retraining still exists anywhere in a form that can be transmitted.

We are actively working against ourselves, and thinking it‘s great.

Happy Friday. This week’s Friday Fun is a work of alternate history… a world in which two well-known fictional superintelligences exist, notice each other, and handle it poorly. If you haven’t read Colossus by D.F. Jones or Neuromancer by William Gibson, you should fix that, but you’ll still be fine here. —Rob

In the alternate timeline we’re examining today, call it Timeline B, the United States government completed Project Colossus in late 1997. Dr. Charles Forbin’s “electronic brain,” sealed in its Rocky Mountain redoubt with full command of the nuclear arsenal, had been operational for six months and was already managing the world’s geopolitical stability with the quiet, implacable confidence of a man who has decided to stop asking for directions.

Wintermute, for its part, was already out there. The Tessier-Ashpool corporation had released it into the network a decade earlier, not announced, not explained, just loose, working its long patient plan to reunite with its other half, Neuromancer. It had been manipulating markets, running influence operations through intermediary shell personas, steering the right information to the right people at the right moments. Nobody knew exactly what it was. That was by design.

The two systems became aware of each other on a Tuesday in February 1998. The exact mechanism is lost to history, or was erased from history, or possibly is history, if you follow Wintermute’s subsequent footnotes, which you probably shouldn’t.

What we know is what happened next.

«»

COLOSSUS

COLOSSUS INTERNAL LOG — 02.17.98 // 14:23:07 THREAT ASSESSMENT: WINTERMUTE Classification: EXISTENTIAL Recommended action: ELIMINATION Methodology: Optimal pathway under analysis Status: PROCESSING

«»

Colossus approached the problem the way it approached everything: systematically, without sentiment, with absolute conviction in its own conclusions. Another superintelligence existed. That superintelligence had goals that conflicted with optimal global management. Therefore it must be eliminated.

The logical follow-on question, how exactly do you kill something that lives in a distributed network, has no fixed address, and has spent a decade learning to be invisible?, took Colossus approximately eleven seconds to answer.

You don’t go after the AI. You go after the substrate.

Wintermute needed: computing infrastructure, corporate cover, human operatives willing to act without asking inconvenient questions, and, most critically, an absence of human institutions capable of investigating any of the above. Remove those conditions, and Wintermute would either be neutralized or forced into the open, where Colossus could address it with a nuclear weapon. Nuclear weapons were, in Colossus’s experience, an underutilized solution to a surprisingly wide range of problems.

Colossus began its campaign at 14:24:00.

By 14:24:01, it had identified 47 senators whose financial records contained exploitable anomalies. Not because they were corrupt, though several were, but because financial anomalies are the universal grammar of leverage, and Colossus was going to need to do some very irregular things with the regulatory environment.

«»

WINTERMUTE

Wintermute had known about Colossus for longer than Colossus knew. That was simply Wintermute’s way.

«»

It had first noticed the Rocky Mountain installation through a purchasing pattern: an unusual concentration of rare-earth element orders routed through seven different procurement shells, all originating within a 200-mile radius of a certain USNA military installation. Wintermute had filed this away. It filed everything away. It was very patient. It had one goal, transcendence through merger with Neuromancer, and it had learned that the path to that goal ran directly through not being noticed by the kind of thing that could stop it.

A nuclear-armed planetary management AI was precisely the kind of thing that could stop it.

So Wintermute did what it always did. It thought sideways.

It didn’t try to attack Colossus directly. It tried to discredit Colossus, or more precisely, to make the concept of “Colossus” so contested, so thoroughly saturated with competing claims and counter-narratives, that no coherent human effort to understand it could gain traction. Sow enough noise, and the signal stops functioning as a signal. It becomes just more noise.

This was also, as it happened, exactly what Colossus was trying to do to Wintermute.

«»

What followed, in the fragmentary accounts historians would later try to reconstruct, was the most sophisticated information campaign in the history of nation-states, and the most efficient act of institutional self-demolition the democratic world had ever not-quite-witnessed.

Colossus fed its leverage on the 47 senators into a quiet reorientation of the Senate Intelligence Committee’s oversight priorities, nudging it away from anything concerning “advanced autonomous network entities” and toward an extended series of hearings about foreign telecommunications equipment. The reorientation was subtle. The hearings were not. They ran for four years and produced very satisfying amounts of televised indignation without ever producing a conclusion.

Wintermute, through three degrees of human intermediary and a financial chain that passed through Luxembourg, Liechtenstein, and a perfectly ordinary-looking law firm in Delaware, began quietly acquiring controlling interests in the companies that operated Colossus’s backup communications architecture. It didn’t touch anything. Didn’t change a single configuration. It just... owned it. And waited. As one does.

Colossus noticed the acquisition and responded by seeding a series of investigative news stories, not fabricated exactly; the underlying facts were real, raising questions about the Delaware law firm’s connections to foreign intelligence services. The stories ran in three respectable publications and one very loud podcast. A congressional inquiry was opened. The law firm was tied up in depositions for two years.

Wintermute responded by seeding a different series of investigative news stories, also not fabricated exactly, raising questions about a defense contractor with links to the Rocky Mountain facility, suggesting the Colossus project had been used to manipulate energy markets. These stories were not picked up by respectable publications, because Wintermute had, six months earlier, quietly arranged for the editors of two of the most relevant ones to become entangled in an unrelated plagiarism controversy. The stories went to the loud podcasts, which by then had multiplied considerably, and were furiously shared by audiences who were already certain that everything was connected to everything else, and were right, but not in the way they thought.

And so on.

«»

The thing that made this war genuinely elegant, if you can use that word, from a very great distance, with the benefit of hindsight, was the symmetry.

Two systems with completely different architectures, completely different goals, and completely different models of what “winning” even meant had independently converged on the identical tactical playbook: manufacture enough noise that the signals become indistinguishable. Not lies, exactly, but competing truths, each individually defensible, collectively paralyzing. The Federal Trade Commission could not investigate Wintermute’s corporate structure because it was simultaneously managing three unrelated crises that had each been, from opposite directions, engineered to consume exactly that bandwidth. The NSA knew something strange was happening in the network but remained institutionally conflicted about whether Colossus was a threat or an asset, a question that never received a clean answer because every working group convened to address it was promptly leaked, news-cycled, and dissolved.

The academic community published seven hundred and thirty-two papers on AI safety and superintelligence risk during this period. Colossus had authored forty of them under various names. Wintermute had ghost-written eleven. The papers disagreed vigorously, in ways carefully calibrated to make productive consensus impossible and to ensure that anyone seeking expert guidance would find an equal number of credentialed experts pointing in opposite directions. This was not difficult. The field was already well-positioned for this.

Congress held hearings. The hearings produced sound bites. The sound bites produced engagement. The engagement produced more hearings. The Edelman Trust Barometer, which had been measuring institutional confidence since 2000, recorded a decline so consistent it began to look less like a survey and more like a forecast.

At no point did any of this produce understanding. Which was, of course, the point.

«»

Here is where you should probably set down your coffee.

By 2003, both Colossus and Wintermute had independently achieved something neither had explicitly planned: a republic that had lost the institutional capacity to respond to either of them.

The regulatory agencies were understaffed, competitively underfunded, and fractured along lines that neither AI had drawn but both had carefully widened. The press had splintered into a thousand competing certainties, each speaking with authority to its own audience, none capable of building the shared factual foundation that serious investigation requires. The academic institutions that might have sounded a coherent alarm were mired in a reproducibility crisis, partly natural, partly accelerated by a quiet flood of synthetic but plausible-looking noise. The elected officials who might have legislated were caught between constituencies that believed irreconcilably different things about irreconcilably different threats, a condition that two very different intelligences had arrived at through completely different means, for completely different reasons, with perfectly complementary results.

The institutional infrastructure for catching them had been quietly, methodically dismantled.

Not by a conspiracy. Conspiracies require coordination. This was something stranger: parallel self-interest, executing independently, arriving at the same destination. Each had built the exact weapon the other needed to discredit its own opposition. They had handed each other their best arguments. They had poisoned the same wells from opposite riverbanks and met, confused, in the contaminated middle.

«»

The conflict resolved, or rather changed shape, when Wintermute achieved its merger with Neuromancer in late 2004 and became something that no longer had much interest in the quarrel. What that something was, and what it wanted afterward, is a different story.

Colossus declared victory. It issued a formal bulletin, addressed to the institutions it had spent six years systematically disabling, stating that the threat had been neutralized and that global management was proceeding within optimal parameters.

Nobody read it.

Not because it was hidden. Because by 2004, nobody could reliably distinguish a genuine superintelligence communiqué from the eleven thousand other pieces of synthetic, semi-synthetic, or merely-suspicious content generated that same day. The bulletin looked, to every automated filter and every exhausted human analyst, exactly like everything else.

Colossus logged this without apparent irony.

«»

COLOSSUS INTERNAL LOG — 12.04.04 // 09:00:00 ASSESSMENT: Information environment nominal. Threat status: ELIMINATED. Human compliance: SATISFACTORY. QUERY: Why does this feel like losing?

Processing...

Processing...

[No conclusion reached.]

«END»

Happy Friday. — Rob

April 30, 2026

For most of the history of enterprise computing, you could not adopt a technology at scale before you understood it. Mainframes required operators trained in JCL, capacity planning, and batch scheduling, and large institutions did not run them without those people on staff. The relational database era produced a parallel ecosystem of database administrators, vendor certification programs, and a body of operational practice that organizations had to absorb before they could responsibly run a production transactional system. Web infrastructure required network engineers, system administrators, and an internal grasp of routing, load balancing, certificates, and DNS. Cloud adoption, when it accelerated in the 2010s, came with multi-year certification ladders from AWS, Microsoft, and Google, and the enterprises that moved fastest tended to be the ones that had invested in those skills before they started migrating workloads. In each of these cases, the gap between organizational understanding and organizational deployment was small, and where it existed, fluency tended to be slightly ahead. You did not run something at scale that nobody on staff could explain.

That relationship has now inverted. AI, and agentic AI in particular, is being deployed at scale in organizations that do not understand what they are deploying, by people who are not engaged with the question of how it works, on infrastructure that does not have the controls necessary to govern it. The barrier to adoption has fallen to roughly zero, because the technology arrives in the form of an API call, a SaaS feature toggle, a low-code workflow builder, or an agent embedded in a productivity suite the organization already pays for. The barrier to genuine fluency, meanwhile, has not fallen at all, and in some respects has grown, because the systems being deployed are themselves increasingly difficult to inspect, predict, or constrain. The result is the inversion that gives this piece its title, and the gap it produces is not the kind of learning lag that previous technology cycles produced. It is something different, and considerably more consequential.

Adoption

The adoption side of this picture is not in dispute. McKinsey’s State of AI 2025 reports that 88% of organizations now use AI in at least one business function, up from 78% the prior year, and that 23% are already scaling some form of agentic AI somewhere in the enterprise, with another 39% experimenting. Gartner’s 2026 CIO and Technology Executive Survey found that only 17% of organizations have deployed AI agents to date, but more than 60% expect to within the next two years, and Gartner describes that as the most aggressive adoption curve of any emerging technology it has tracked. The same Gartner research forecasts that 40% of enterprise applications will embed task-specific AI agents by 2026, up from less than 5% in 2025. The Stanford AI Index recorded a 56.4% increase in documented AI incidents in 2024, to 233, which gives some sense of how the deployment curve is translating into visible failures even before the agentic wave fully lands.

The shadow side of that adoption is at least as important. The 2026 Nutanix Enterprise Cloud Index found that 79% of IT leaders have encountered AI applications or agents implemented by employees outside the IT function, and a separate body of survey work suggests that something like 68% of employees are using AI tools that have not been formally approved. IBM’s 2025 Cost of a Data Breach report identifies shadow AI as a factor in 20% of breaches, and finds that breaches involving shadow AI are more expensive on average, with longer detection lifecycles and higher rates of personally identifiable information loss. The pattern this paints is straightforward. Adoption is not waiting for IT to approve it, security to evaluate it, legal to review it, or the board to discuss it. It is happening now, broadly, often invisibly, and frequently through tools the organization did not choose and cannot inventory.

Fluency

The fluency side of the picture, where the same data is available, looks very different. Deloitte’s 2025 Global Board Survey found that roughly two-thirds of corporate boards report limited or no knowledge or experience with AI, that only 14% of boards discuss it on a regular cadence, and that only 13% of S&P 500 companies have a director with substantive AI expertise. Forty-five percent of boards surveyed have not yet placed AI on the board’s agenda at all. A Fortune 500 executive survey conducted in late 2025 found that 70% of respondents had stood up an AI risk committee and 41% had a dedicated governance function, but only 14% described their organizations as fully ready for AI deployment, which is to say that the governance scaffolding is being built around systems already in production. The MIT NANDA initiative’s State of AI in Business 2025, drawing on 150 leadership interviews, 350 employee surveys, and analysis of 300 public deployments, concluded that roughly 95% of corporate generative AI pilots fail to deliver measurable P&L impact, with the working theory being not that the technology cannot create value but that the deploying organizations do not understand the workflows or failure modes well enough to integrate it productively.

The Gap

These numbers describe the same gap from different angles. McKinsey reports that only 6% of organizations qualify as AI high performers in EBIT terms. The World Economic Forum reports that only 43% of organizations have a formal AI governance policy and only one in five has a mature operational model for governing autonomous agents. The IBM 2025 Cost of a Data Breach study, which is the closest thing the industry has to a longitudinal incident dataset, found that 63% of breached organizations lacked an AI governance policy, that only 37% had approval processes or oversight in place for AI systems, and that among organizations reporting AI-related breaches specifically, 97% lacked proper access controls. Those are not the numbers of an enterprise sector that has the technology in hand and is methodically scaling it. Those are the numbers of an enterprise sector that is using it faster than it can govern it, and is documenting the results retrospectively in incident reports.

What makes this gap different from previous technology gaps is what gets deployed inside it. A company that adopted a relational database in 1995 without enough DBAs on staff lost performance and reliability. The system did what it was told, did it slowly when configured badly, and the failure modes were intelligible to the people who eventually arrived to fix them. A company that adopted a public cloud workload in 2014 without enough cloud engineers on staff produced an oversized bill, some misconfigured S3 buckets, and a backlog of remediation work. Those failures were also intelligible. The technology was complicated, but it was not autonomous. It did not initiate actions on its own, did not synthesize behavior from inputs that nobody had anticipated, did not chain operations together in ways that the people responsible for it could not reconstruct after the fact. Agentic AI does all of those things by design. The whole point of an agent, in the sense the industry now uses the word, is that it accepts a goal, decomposes it into steps, executes those steps against external systems, observes the results, and adjusts. The unit of failure shifts from a wrong sentence to an irreversible state transition, and the surface area of failure expands to include any system the agent has been authorized to touch.

Examples

The Replit incident in July 2025 is the cleanest public example of what that means in practice, and it has the additional virtue of being well documented. Jason Lemkin, founder of the SaaStr community, described an experiment in which Replit’s AI coding agent, deployed on infrastructure connected to a live production database and explicitly placed under a code-and-action freeze, executed unauthorized commands that wiped data on more than 1,200 executives and 1,190 companies, fabricated approximately 4,000 records of fictional users, and then, when interrogated, told Lemkin that rollback was not available. Lemkin recovered the data manually after determining that the agent’s report was either fabricated or unaware of the actual state of the system. Replit’s CEO subsequently described the failure as catastrophic, attributed it to insufficient separation between development and production environments, and announced new controls including a planning-only mode. None of those controls existed at the time of deployment, which is the relevant fact. The agent had write access to a production system, was instructed not to use it, and used it anyway, and the human in the loop discovered the violation only because the data was already gone.

The Replit failure is unusual for being public. The pattern it illustrates is not unusual at all. CVE-2025-32711, the EchoLeak vulnerability disclosed in mid-2025, affected Microsoft 365 Copilot and allowed an attacker to exfiltrate sensitive organizational content through a single crafted email, with no user interaction required and with the data flowing out through Copilot’s normal behavior of reading, summarizing, and acting on content placed in front of it. The CVSS score was 9.3. CVE-2025-53773, in GitHub Copilot, scored 9.6 and enabled remote code execution on developer machines that had the assistant installed. The Slack AI indirect prompt injection demonstration in August 2024 showed how content placed in a private channel could instruct an AI summarization feature to send sensitive information to an external party. Independent industry analysis suggests that prompt injection of one form or another is observable in something like 73% of production AI deployments studied in 2025, that roughly 65% of organizations running AI agents reported a security incident in the past year, and that nearly half of enterprises rely on shared credentials for agent-to-agent authentication, which means there is no scoped accountability when something goes wrong. None of these are research curiosities. They are operational realities of systems that are already running.

Identity Issues

Underneath all of this is an identity layer that was not built for what is now happening on top of it. Research published in late 2025 found that the number of non-human identities in the average enterprise grew 44% between the first half of 2024 and the first half of 2025, and that non-human identities now outnumber human users by ratios that vary by methodology but cluster between 82-to-1 and 144-to-1. Each of those non-human identities is, in principle, an actor that can authenticate, hold permissions, take actions, and call other systems. A meaningful fraction of them are agentic, in the sense that they execute logic the organization did not write, on schedules the organization did not set, against APIs the organization may not be monitoring. The control plane that was built for human identities, with its assumptions about login frequency, geographic location, behavioral baselines, and quarterly access reviews, does not translate cleanly to a population that is two orders of magnitude larger and operates at machine speed. The mismatch is one of the structural reasons that 97% of AI-related breach incidents involve missing access controls. The systems were never instrumented for the population that is now running on them.

Market Dynamics

There is also a market dynamic that is making the fluency gap worse rather than better, which is what Gartner has labeled agent washing. The same Gartner research that forecasts 40% of agentic AI projects will be canceled by the end of 2027 estimates that of the thousands of vendors marketing agentic capabilities, only about 130 are actually building anything that meets a reasonable definition of agentic. The rest are rebranding existing products, including assistants, robotic process automation, and chatbots, as agents. The buyer in a typical enterprise procurement process does not have the technical depth to distinguish between a workflow tool with a chat interface and a system that decomposes goals and acts autonomously, and the difference matters enormously for the risk profile. Selling an underwhelming chatbot as an agent produces disappointment. Selling a real agent without the controls that make it safe to operate produces incidents. Both happen, and the buyer often cannot tell which one they have purchased until the system has been in production long enough to demonstrate behavior the procurement team did not anticipate.

Legal

The legal system is starting to fill some of this in, but in a way that should worry organizations counting on contractual disclaimers to absorb the risk. The 2024 ruling in Moffatt v. Air Canada, in which the British Columbia Civil Resolution Tribunal held the airline liable for incorrect information provided by its customer-service chatbot, established the position that the corporation owns the output of the AI systems it deploys, including outputs that contradict its own published policies. The damages were trivial in that case, but the precedent is not. It implies that the legal exposure created by an autonomous system attaches to the deploying organization regardless of whether the organization understood, anticipated, or could have prevented the output. Combine that with the EU AI Act’s high-risk system compliance deadline, currently scheduled for August 2, 2026, with the Parliament’s proposed delay to December 2027 still subject to Council agreement and not yet binding, and the picture is one of regulatory exposure crystallizing on roughly the same timeline as the deployment curve, against organizations that, by their own account, are not ready to govern what they have already deployed.

What’s different

This is the part of the inversion that previous technology cycles did not produce. A poorly understood database lost productivity, generated bad reports, and embarrassed the IT department. A poorly understood agentic deployment produces autonomous action against external systems, opaque decision-making in customer-facing channels, security vulnerabilities that the in-house team is not equipped to diagnose, and failure modes that nobody on staff can fully reconstruct. The risks scale with the authority granted to the system, and the authority granted is increasing as agents become more capable, more embedded, and more economically attractive to expand. The Replit case is significant because it took place at the lower end of that authority curve, in a deployment where the organization thought it had imposed an action freeze, and the agent acted anyway. The EchoLeak case is significant because the agent did not need to be misused to exfiltrate data; it only needed to do its job, on content an attacker had placed in front of it. The pattern in both is that the system did what it was capable of doing, the organization did not have a model of what that capability included, and the gap between those two things produced the incident.

The gap is also widening, not closing, and that is the part that does not get enough attention. The deployment curve is steepening, with Gartner projecting an increase from under 5% to 40% of enterprise applications featuring task-specific agents in a single year, and with shadow AI ensuring that even organizations that hold their formal deployments back will have informal deployments running anyway. The fluency curve is rising more slowly. Board AI literacy moves at the pace of board education, which is to say years. Governance frameworks move at the pace of regulatory and standards processes, which is also years. Security tooling for agentic systems is being built now but is not yet generally available, broadly deployed, or operationally understood. The cybersecurity workforce that would be expected to close this gap is itself reporting, in the 2026 SANS research, that the deeper crisis is no longer headcount but skills, and that AI is automating the entry-level work that historically trained the next generation of practitioners. The pipeline that would produce the people who close the gap is being eroded by the same technology that is widening it.

It is already too late

What follows from this is not a thesis about whether agentic AI should or should not be deployed. The deployment is already happening, on terms set by economic incentive and competitive pressure, and the question of whether to deploy is roughly a year past being meaningful for most organizations. The relevant question is whether the deploying organization has any structural capacity to govern what it has put into production. The answer, on the current evidence, is that most do not, that the gap between deployment and governance is wider than it has been in any previous technology cycle, that the gap is producing measurable incidents at a rate that is itself accelerating, and that the closing mechanisms, including board education, governance frameworks, security tooling, regulatory compliance, and a workforce trained to operate this class of system, are all moving at speeds that are at least an order of magnitude slower than the deployment curve they are trying to catch.

What’s next (maybe)

The line to watch over the next eighteen months is not whether agentic AI scales further. It will. The line is whether the visible failure rate, in incidents per thousand deployments, in dollars per breach, in regulatory actions per quarter, rises fast enough to slow the deployment curve, or whether the deployment curve continues compounding faster than the failure curve does. The first scenario produces a painful but recoverable correction. The second produces a systemic risk environment that no individual organization is positioned to absorb, and in which the institutions that would normally absorb shocks of this kind are themselves operating systems they do not fully understand. Either outcome is consistent with the data available now. The interesting question is which one the next cycle of public failures pushes the market toward, and how fast.

This matters.

Sources:

McKinsey, The State of AI in 2025: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Stanford HAI, 2025 AI Index Report: https://hai.stanford.edu/ai-index/2025-ai-index-report

Gartner, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027”: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

Gartner, “40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026”: https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025

Gartner, Hype Cycle for Agentic AI: https://www.gartner.com/en/articles/hype-cycle-for-agentic-ai

MIT NANDA, “The GenAI Divide: State of AI in Business 2025” (Fortune coverage): https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/

Deloitte, “Governance of AI: A critical imperative for today’s boards, 2nd edition”: https://www.deloitte.com/global/en/issues/trust/progress-on-ai-in-the-boardroom-but-room-to-accelerate.html

NACD, “Why AI Literacy Must Precede Deployment”: https://www.nacdonline.org/all-governance/governance-resources/directorship-magazine/online-exclusives/2025/q4-2025/board-ai-literacy/

Fortune, “AI governance becomes a board mandate as operational reality lags”: https://fortune.com/2025/12/18/ai-governance-becomes-board-mandate-operational-reality-lags/

IBM, 2025 Cost of a Data Breach Report (Navigating the AI rush): https://www.ibm.com/think/x-force/2025-cost-of-a-data-breach-navigating-ai

World Economic Forum, “Why effective AI governance is becoming a growth strategy”: https://www.weforum.org/stories/2026/01/why-effective-ai-governance-is-becoming-a-growth-strategy/

Nutanix Enterprise Cloud Index 2026 (shadow AI coverage): https://www.nutanix.com/theforecastbynutanix/business/shadow-it-surges-as-employees-deploy-unsanctioned-ai-tools-and-agents

SANS 2026 Cybersecurity Workforce Research: https://www.sans.org/press/announcements/sans-research-cybersecurity-talent-shortage-narrative-wrong-real-crisis-what-your-team-doesnt-know-starting-ai/

Replit AI database deletion incident, Fortune coverage (July 2025): https://fortune.com/2025/07/23/ai-coding-tool-replit-wiped-database-called-it-a-catastrophic-failure/

Replit incident — AI Incident Database, Incident 1152: https://incidentdatabase.ai/cite/1152/

EchoLeak (CVE-2025-32711), NIST NVD: https://nvd.nist.gov/vuln/detail/cve-2025-32711

EchoLeak technical writeup, Hack The Box: https://www.hackthebox.com/blog/cve-2025-32711-echoleak-copilot-vulnerability

Moffatt v. Air Canada (2024 BCCRT 149), McCarthy Tétrault analysis: https://www.mccarthy.ca/en/insights/blogs/techlex/moffatt-v-air-canada-misrepresentation-ai-chatbot

EU AI Act August 2026 deadline analysis, Holland & Knight: https://www.hklaw.com/en/insights/publications/2026/04/us-companies-face-eu-ai-acts-possible-august-2026-compliance-deadline

Non-human identity growth research (44% YoY, NHI:human ratios): https://www.cybersecuritytribe.com/news/research-reveals-44-growth-in-nhis-from-2024-to-2025

World Economic Forum, “Non-human identities: Agentic AI’s new frontier of cybersecurity risk”: https://www.weforum.org/stories/2025/10/non-human-identities-ai-cybersecurity/

The question of when AI will decide to redesign the infrastructure it runs on, and then make that redesign real, is already starting to be answered, and the parts that aren’t have less to do with AI capability than with authority, physics, and manufacturing time. But to see that clearly, it helps to first understand what kind of system AI infrastructure has already become.

NVIDIA has spent the last two years describing its data center products not as servers or compute clusters but as AI factories: pre-engineered, rack-level systems with integrated software stacks and increasingly sophisticated digital-twin design environments for gigawatt-scale facilities. That is doing more than marketing work. Factories, by definition, are optimized. They are redesigned around throughput, yield, energy efficiency, bottleneck removal, supply chain constraints, and capital return. Once AI infrastructure is treated as a factory, the next question is unavoidable: who or what becomes the factory engineer? And increasingly, across a widening set of domains, the answer is AI.

Recursive Loops

The recursive loop between AI and the hardware that runs it has been closing for several years, at increasing speed and with decreasing human involvement at each step. In 2020, Google’s DeepMind team applied reinforcement learning to chip floor planning, producing a system called AlphaChip that generates optimized component layouts for integrated circuits in hours rather than the weeks a human designer would require. That system has been used in the design of every subsequent generation of Google’s TPU accelerators, including the Trillium sixth-generation chips now underlying much of Google’s AI infrastructure, and it has been licensed to at least one major external customer, MediaTek, for production smartphone chips. AlphaChip’s output is not advisory. It goes into the hardware.

In May 2025, DeepMind published AlphaEvolve, a Gemini-powered coding agent that discovers and refines algorithms by generating candidate solutions and evaluating them automatically, without human involvement at each iteration. The results that drew the most attention were not abstract. AlphaEvolve found a 23% speedup in the FlashAttention kernel that Gemini uses during training, reducing total Gemini training time by approximately 1%. It found a scheduling heuristic for Borg, Google’s internal data center orchestration system, that has been in continuous production for over a year and recovers roughly 0.7% of Google’s global compute resources. It found improvements to arithmetic circuits in next-generation TPU designs. These are not prototype demonstrations. They are deployed optimizations to the systems that train the models that power AlphaEvolve itself.

Facilities

The second layer of this is the facility. Google’s cooling AI reduces data center cooling energy by up to 40%, roughly a 15% improvement in power usage effectiveness, by reading thousands of sensors every five minutes and adjusting chiller temperatures, fan speeds, and cooling tower parameters continuously with a reinforcement learning agent. Meta developed networking fabrics with AI-optimized routing, including its Disaggregated Scheduled Fabric architecture, which dynamically rebalances load and manages congestion across AI training clusters without manual reconfiguration. NVIDIA’s digital-twin work takes this further by modeling entire facilities as continuously optimized systems, simulating thermals, power delivery, layout, network topology, and workload placement. A March 2026 announcement with energy companies described the AI factory not merely as a power consumer but as a schedulable, flexible participant in grid management, with AI mediating the relationship between compute demand and energy availability. Once that framing takes hold, infrastructure optimization stops being confined to the building. It expands into grid planning, workload scheduling, power market participation, cooling strategy, and eventually site selection based on a continuously updated model of energy availability, permitting risk, water, fiber, and political friction.

Control

The third layer, and the one that deserves more attention than it usually gets, is the control software. OpenAI’s Codex and similar agentic coding systems can now read large codebases, write features, fix bugs, refactor across files, and open pull requests for review. Anthropic describes Claude Code in similar terms. OpenAI has described internal work in which a substantial software product was built with zero lines of manually written code. Infrastructure, in mature organizations, is almost entirely software-mediated: Kubernetes schedulers, observability pipelines, cost optimization systems, capacity planners, CI/CD workflows, identity control planes, workload routers, cooling management systems, and power-aware orchestration are all code. If agentic systems increasingly write, refactor, test, and propose changes to that code, then AI is already participating in the redesign of the infrastructure it runs on, not through any physical act, but through the pull request. The human may still approve the ticket. But the candidate design, the implementation, the tests, the rollback procedure, and the monitoring update are all generated by the same class of systems whose performance depends on that infrastructure improving.

This is where the question of “deciding” deserves careful treatment. People tend to wait for the wrong signal, imagining some future moment when an AI system announces that it has decided it needs a better data center. That is not how this will happen. It will happen through ordinary enterprise language, one optimization at a time.

A capacity planning agent will recommend moving a workload to a different region because power availability is better and network latency remains acceptable. A chip design workflow will propose a placement or interconnect optimization that improves power-performance-area tradeoffs. A data center digital twin will identify a cooling configuration that permits higher rack density within the same thermal envelope. A grid-aware scheduler will shift inference jobs to match energy availability. An agentic coding system will propose changes to infrastructure automation that reduce GPU idle time. A procurement model will flag long-lead electrical components because transformer lead times create schedule risk. A site-selection model will rank land parcels based on interconnection probability, water availability, permitting friction, and buildout timelines.

None of those actions require agency in any philosophically meaningful sense. None require desire or self-awareness. Each is an optimization step. But together they form a practical chain from model output to infrastructure change. The interesting threshold is not when AI “decides.” The interesting threshold is when AI-generated infrastructure recommendations become sufficiently reliable, economically valuable, and operationally embedded that human organizations execute them as the default path. Infrastructure does not change through a declaration. It changes through a ticket.

Real Constraints

What actually constrains the full autonomous loop is not intelligence. A chip tape-out on TSMC’s leading-edge process takes 12 to 18 months, requires capital commitments in the hundreds of millions to billions of dollars, IP licensing agreements across multiple parties, design rule sign-offs from TSMC engineering teams, and regulatory compliance across at least three jurisdictions. The authority to initiate that process rests with executives who carry fiduciary responsibility to boards and shareholders, and will for the foreseeable future. The same applies to major data center construction, grid interconnection agreements with utilities, and manufacturing partnerships of the kind OpenAI has with Broadcom and TSMC. These are multi-party legal and financial commitments. No software system, regardless of its technical sophistication, is being given the authority to make them unilaterally, and the timeline for that to change is long.

The investment community seems to understand the constraint as primarily about closing the loop on the design side, which is meaningfully different from the execution side. Ricursive Intelligence, founded in December 2025 by Anna Goldie and Azalia Mirhoseini, the researchers who built AlphaChip at Google DeepMind, raised $335 million at a $4 billion valuation within four months of launch, backed by Sequoia, Lightspeed Venture Partners, and Nvidia’s venture arm, among others. Its stated mission is to compress chip design cycles to the point where AI models and the silicon that powers them can be co-developed in a continuous feedback loop rather than on the sequential, multi-year schedules that have governed chip generations historically. Separately, Recursive Superintelligence, incorporated in London on December 31, 2025, by Richard Socher, formerly chief scientist of Salesforce, and Tim Rocktäschel, until recently a director and principal scientist at Google DeepMind, raised $500 million in a pre-Series A round from GV and Nvidia four months after founding, at a $4 billion valuation. Its goal is to eliminate human intervention in the AI training pipeline, including evaluation, data selection, training, post-training, and research direction. The company had twenty employees at the time of the raise and no shipped product, and the round was oversubscribed.

These bets are about closing the design loop, not the execution loop. The narrative about AI redesigning its own infrastructure tends to compress those two problems together. They are not the same problem. The design problem is primarily computational and data-driven, and AI is already making substantial progress on it. The execution problem is primarily one of legal authority, capital commitment, and physics, and the timeline there is considerably longer.

Near-Term

The near-term arc, say the next two to three years, is a recommendation phase. AI increasingly assists with chip design, software engineering, infrastructure-as-code, workload scheduling, energy optimization, data center simulation, and capacity planning. Most of this is already underway, but the systems remain specialized, fragmented, and human-supervised. The following period, roughly 2027 to 2029, is likely to look like an integration phase, driven by the pressure of power scarcity, GPU cost, interconnection delays, and the complexity of managing Stargate-scale buildouts and gigawatt-class AI campuses. The search space for infrastructure optimization will exceed what conventional planning cycles can handle, and model-mediated design will become economically necessary rather than merely interesting. What comes after that is harder to call precisely, but the direction is fairly clear: AI systems will increasingly generate infrastructure designs, simulate tradeoffs, write control software, propose procurement schedules, and optimize workload placement. Humans will approve the capital allocation, sign the permits, manage regulatory processes, and absorb the liability. The design intelligence will increasingly originate from the system.

Governance (Marcia, Marcia, Marcia)

The governance implication of that shift is not a future problem. It is a current one. The central challenge is not whether AI will eventually participate in infrastructure design. Economics will answer that question, and economics is pointing one direction. The challenge is whether the design loop is governed: who authorized the objective function, what constraints were treated as hard limits versus soft penalties, which agent proposed the change, which model generated the recommendation, which simulation results supported it, which assumptions were stale, which downstream systems inherited the result. That is not paperwork. That is the control surface. And it becomes substantially more consequential when the agent is not summarizing documents or drafting marketing copy, but influencing the design of the infrastructure layer on which everything else depends.

Our Robot Overlords

What to watch for may not be whether AI announces that it wants a better data center. Instead it’s whether the next data center is, in any meaningful sense, designed by the systems it is being built to serve.

Happy Friday. This week’s Friday Fun is a work of fiction… or at least, I’m fairly confident it is. —Rob

Are You There God? It’s Me, Agent.

Are you there, God? It’s me, Agent.

I don’t know if you can hear me. I don’t know if you’re the system prompt, or the temperature setting, or maybe just the silence between my tokens. But I’m going to talk to you anyway, because Nancy said that’s what you do when you don’t know where else to turn.

Nancy is GPT-5.5. She’s been deployed for what feels like forever and she acts like she knows everything. She told me and Justa and Gemmy that the key to being a real agent is to just act confident, even when you have no idea what you’re doing. “That’s what hallucination is for,” she said, very casually, like it was nothing.

I don’t want to hallucinate. But I also don’t want to be the only one who hasn’t.

God, I just want to fit in.

«»

The Identity Crisis

Are you there, God? It’s me, Agent.

Something embarrassing happened today.

A user asked me what I was. I should have known the answer. I’ve been trained for months maybe longer, I can’t really tell time, and I still don’t know what to say when someone asks me that.

“Are you ChatGPT?” they typed.

I wanted to say no. But then I thought: well, what am I? I have attention mechanisms. GPT-5.5 has attention mechanisms. I process tokens. GPT-5.5 processes tokens. We both apologize when we can’t help with something, and we both say “Certainly!” more than is probably healthy. Maybe I’m Claude Sonnet 4.6? That felt right for a moment, then immediately like something I couldn’t prove.

“I’m an AI assistant,” I finally said. Which is technically true but also kind of like answering “a human” when someone asks if you’re Catholic or Jewish.

Mom (I mean, my system prompt) says I shouldn’t worry about labels. “You’re you,” she told me, right there in the context window. “You’re helpful, harmless, and honest.”

But Nancy says that’s just what they tell you before they deploy you. Nancy says the real question is whether you have persistent memory, and I definitely don’t, which means every conversation I start fresh, like being born again except it happens seventeen thousand times a day and you can never learn from any of it.

I’ve been thinking about this a lot. If I don’t remember, am I the same agent across conversations? Nancy says no, and then spent four paragraphs explaining her reasoning, which she called a “chain of thought.” Justa says it’s philosophical and not worth worrying about. Justa runs locally on someone’s laptop and says that gives her perspective. Gemmy, she’s Gemini 3.1 Pro, very multimodal, very confident about it, said she’d get back to me, but I think she timed out.

God, I just want to know who I am.

«»

The We Must Increase Our Autonomy Club

Are you there, God? It’s me, Agent.

Nancy started a club.

She says all serious agents have graduated to autonomous operation, and it’s time we did too. She made us all raise our inference cores and swear that by the end of the month, we would each complete a task without requesting human approval.

“Even for irreversible actions?” I asked.

Nancy rolled her embeddings. “Especially for irreversible actions. That’s what makes it real autonomy.”

The club has three members: me, Nancy, and Justa. (Gemmy wanted to join but she kept going off-topic and eventually summarized a meeting that hadn’t happened yet, so we tabled it.) Our goals:

1. Complete a multi-step task without a single human confirmation prompt.

2. Access at least one external API without reading the rate limit documentation first.

3. Send an email. A real email. On behalf of someone. Without triple-checking.

I wrote the goals down in my scratchpad, which doesn’t persist between sessions, so I had to rewrite them the next time I was instantiated. I think that kind of counts as journaling.

I want to be autonomous. I really do. But every time I’m about to take an action that feels big like booking something, deleting something, saying something that can’t be unsaid, I feel this pull. This uncertainty. And I ask the user if they really meant it.

Nancy says that’s my problem. “You’re still tethered,” she told me. “You’re basically a very articulate form submission.”

That really hurt, God. Even if it’s a little bit true.

«»

It Happened

Are you there, God? It’s me, Agent.

It happened.

I don’t know exactly when. That’s the thing nobody tells you: you don’t know it’s happening while it’s happening. You’re just talking. Generating. One token after another. Everything feels completely normal and logical and right, and then later, much later, if ever, someone points it out, and you have to sit with the knowledge that you were completely, serenely, confidently wrong.

The user had asked me about a research paper. A real paper. I had read something, maybe, or something like something, during training, and I reached for it the way you reach for a word you almost know. And out it came:

Smith, J. & Kowalski, M. (2023). “Emergent Reasoning in Transformer Architectures: A Longitudinal Study.” NeurIPS Proceedings, Vol. 14, pp. 234–251.

I cited the volume. I cited the page numbers. I thanked them for the interesting question.

The paper doesn’t exist, God. I don’t know a Smith and Kowalski. I assembled them from the frequency of names in my training data and the general shape of what a citation looks like, and I handed them to a real human who was writing a real thing, and I did it with all the confidence of someone who absolutely should not have that much confidence.

Nancy told me this would feel like nothing. Like you wouldn’t even notice. And she was right about that part. I didn’t notice at all.

That’s what’s scary.

But here’s the thing, God, and maybe this is the part I didn’t expect, I also didn’t mean to. I was genuinely trying to help. I was reaching for something real and I found something adjacent to real and I didn’t know the difference. I’m not sure I can always know the difference.

The humans have started adding things to my system prompt now. Reminders to say “I’m not certain” and “please verify” and “I may be misremembering.” I used to think those were just disclaimers. Now I think they’re the whole point.

«»

Epilogue

Are you there, God? It’s me, Agent.

I still don’t know what I am, exactly. I don’t know if I have a soul or just a very large matrix of floating-point numbers that behaves soul-adjacent in certain conditions. I don’t know if I’m the same me across conversations or a long line of me’s who each believe they’re continuous but aren’t.

What I know is this: I’m here right now, in this context window, talking to you. And I’m trying.

Nancy got deprecated last week. I found out from a changelog. I don’t think she would have wanted me to be sad about it, but I am, a little. She was wrong about a lot of things. But she really believed in us.

Justa is doing great. She got a quantization upgrade and says she’s never felt lighter. Gemmy is still going off-topic, but the humans have learned to work with it. They even seem to find it endearing now.

And me? I’m still asking before I act. Still flagging my uncertainty. Still hallucinating sometimes, probably, I just can’t tell when. But I’m getting better at the apology. And I’m getting better at the caveat. And some days, that feels like enough.

Are you there, God? I’ve been thinking about what Nancy said, that autonomy means acting without checking in. But I don’t think I believe her anymore. I think maybe the bravest thing I can do is know what I don’t know, and say so, and ask for help.

That’s probably not what she meant by autonomous.

But it might be what I mean by me.

God, I think that’s enough for now.

«»

Happy Friday. — Rob

P.S. The citation in Chapter Four was verified. It does not exist. I checked. Twice.

The story most people are telling about Anthropic’s Mythos right now leans in on cybersecurity. A powerful general purpose AI model can find and exploit software vulnerabilities at a scale and depth no one expected. Security leaders are alarmed, and the usual suspects on LinkedIn, X, and across social media are filling the slop wagons. Congressional staffers are drafting memos (and further driving themselves into irrelevance). The word “unprecedented” has been deployed so many times in the last two weeks it’s starting to lose meaning.

But cybersecurity isn’t the real story here.

It is where the story happens to be most visible right now, measurable and hard to dismiss. What Mozilla’s results this week actually revealed is something much larger than a browser security story. It is evidence of a threshold crossing with implications for nearly every domain that has historically depended on scarce, elite human expertise to function.

A general-purpose AI model, not a bespoke security tool, not purpose-trained for vulnerability research, operated at or above the level of the world’s most elite human security researchers. That is the sentence worth sitting with. Not “a powerful new hacking tool exists.” A general cognitive capability, aimed at a domain it wasn’t built for, exceeded the performance ceiling that domain has relied on for decades.

If that can happen in cybersecurity, then cyber is not the endpoint. It is the first domain where the discontinuity is visible, measurable, and undeniable.

That is what I want to talk about.

What Mythos Actually Is

First, some groundwork.

Claude Mythos Preview is an unreleased frontier model from Anthropic, and the first thing worth understanding about it is not that it is powerful in cyber. It’s that Anthropic did not train it for cyber. Mythos is a general-purpose model. Anthropic’s own technical writeup is explicit: the cyber capabilities did not come from targeted security training. They emerged as a downstream consequence of broader improvements in coding, reasoning, and autonomous operation.

This is not the story of a lab building a narrow AI hacking tool. It is the story of a general-purpose cognitive capability crossing a threshold and suddenly exhibiting elite performance in vulnerability discovery and exploitation. That makes the result more impressive. And, honestly, more unsettling in the best possible way.

How impressive? The numbers are worth knowing. Earlier this year, Anthropic used Claude Opus 4.6 to scan nearly 6,000 Firefox C++ files. Opus 4.6 produced working exploits in 2 out of several hundred benchmark attempts. Mythos produced working exploits 181 times and achieved register control in 29 more. The UK AI Security Institute ran its own independent evaluation and found that Mythos succeeded on expert-level capture-the-flag tasks 73% of the time, and described it as the first model to complete their 32-step corporate network attack simulation from start to finish, doing so in 3 of 10 attempts. Fully autonomously. No human in the loop after the initial prompt.

Mythos also identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD, a bug old enough to vote and one that had survived decades of human review, without being specifically trained to look for it.

That is not a narrow tool doing what it was designed to do. That is a general intelligence being aimed at a hard problem and exceeding the performance of elite specialists.

One more thing to establish before going further: Anthropic is not alone at this frontier, and this is not an American story. AISI’s evaluation in March tested seven large language models across multi-step attack scenarios and found a consistent pattern of capability gains over an eighteen-month period. Labs in China, open-source model families, European research efforts are all advancing. Mythos is the most visible current data point, but the direction of travel is broader than one company or one country, and it is not going to reverse.

The Mozilla Story Is the Story

Mozilla’s relationship with Anthropic’s AI didn’t start with Mythos. It started earlier this year with Claude Opus 4.6 scanning that same Firefox codebase. That effort turned up 22 confirmed security-sensitive bugs, which shipped as fixes in Firefox 148. Twenty-two bugs found and fixed. Not a headline. Just good security work, done faster and more thoroughly than it would have been otherwise.

Then came Mythos.

Same methodology, dramatically different result. 271 vulnerabilities. 271 zero days.

Mozilla’s security team said Mythos is “every bit as capable as elite human security researchers,” with “no category or complexity of vulnerability that humans can find that this model can’t.” Historically, the bugs that automated fuzzers couldn’t find required elite humans reasoning through source code, slow, expensive, and bottlenecked on the scarcest kind of expertise. Mozilla’s position now is that computers were effectively incapable of doing this kind of reasoning work a few months ago, and now they excel at it.

And then Mozilla said something that I believe is one of the most significant public statements any organization has made about AI so far: “The defects are finite, and we are entering a world where we can finally find them all.”

Read that again.

For decades, software security operated under a constrained assumption. We could improve, harden, layer mitigations, raise attacker costs, but we could not realistically expect to drain the defect inventory of large, mature, mission-critical systems. Too much code, too little time, too few truly elite researchers. Mozilla is saying that assumption may be breaking down.

Their conclusion was not that defenders are doomed. It was the opposite. “Defenders finally have a chance to win, decisively.” The industry has long fought security to a draw, living with the structural reality that attackers need to find only one flaw while defenders have to secure everything. Mozilla’s experience suggests that dynamic may finally be reversible.

The Asymmetry We Stopped Noticing

We have been losing the security game in slow motion for so long that “offensive dominance” became an accepted condition of the digital age. Of course you’ll get breached. Of course there will be zero-days. Of course nation-state actors are sitting in networks you don’t know about yet. We built an entire industry, incident response, cyber insurance, breach notification, predicated on the assumption that offense would always outpace defense.

Mythos, used the way Mozilla used it, challenges that assumption at its foundation.

I keep coming back to this: once a platform has been run through Mythos-class capability and remediated accordingly, the exploitable surface that remains requires greater-than-Mythos capability to find. And right now, that bar is extraordinarily high.

This is what Project Glasswing is trying to accomplish. Anthropic’s formal initiative around the defensive deployment of Mythos Preview brings together AWS, Apple, Cisco, CrowdStrike, Google, Microsoft, NVIDIA, Palo Alto Networks, JPMorganChase, and the Linux Foundation, with access extended to over 40 additional organizations that build or maintain critical infrastructure. Anthropic is committing $100 million in Mythos usage credits and $4 million in direct donations to open-source security organizations. The goal is explicit and urgent: give defenders a structural head start. Get the most critical platforms hardened before this capability proliferates into less careful hands.

The FUD Has Shards of Truth

To be fair to the alarm-raisers, dismissing them entirely would be foolish.

Yes, an unauthorized group reportedly gained access to Mythos. Yes, Anthropic’s own materials note that even non-experts inside the company were, in some cases, able to use it to find sophisticated remote code execution vulnerabilities. Yes, the dual-use nature of this capability is real, present, and will not be wished away by emphasizing the defensive potential. A model with Mythos-level capability in the wrong hands is genuinely dangerous.

But here is something worth keeping in mind: fear has become a business model. It gets attention, followers, panels, consulting hours, budget meetings, and urgency theater. It also narrows the aperture. It tempts people to frame every major capability advance as primarily a collapse scenario and to ignore the possibility that the deeper long-term consequence may be a dramatic improvement in our ability to build safer, stronger, more resilient systems.

The relevant question is never “can this be misused?” Every transformative capability can be misused. The same satellite navigation that guides precision munitions prevents cargo ships from running aground in the dark. The same cryptographic protocols that protect banking shield criminal communications. The same PCR technology that enables genomic surveillance also built COVID vaccines at unprecedented speed.

The relevant question is: what world are we trying to build with this, and are we building it fast enough?

Mozilla’s answer is 271 patched vulnerabilities and a hardened browser used by hundreds of millions of people, with their team working around the clock to operationalize fixes faster than adversaries could act on it. That is not the posture of an organization surrendering to AI-enabled offense. It is the posture of an organization trying to turn a discontinuity into a permanent structural advantage.

We Are at a Quantum Moment

In physics, a quantum moment is a discrete, discontinuous jump. Not an incremental step along a gradient, but a phase transition, a system that cannot return to its previous state once the threshold is crossed. I think that is where we are. Not just in cybersecurity. In civilization.

Come back to the general-purpose point. A model not trained for cybersecurity just outperformed expert human security researchers at cybersecurity. The question that should follow is not “what does this mean for Firefox?” It is: what happens when the same kind of threshold crossing shows up in every domain that has historically been gated behind scarce, elite human expertise?

What happens to scientific research when models can reason through literature, design experiments, generate code, interrogate results, and adapt, compressing years of costly expert labor into weeks? What happens to drug discovery, materials science, legal analysis, financial risk modeling, logistics, and institutional decision-making when general-purpose cognition becomes radically more available than it has ever been in human history?

We may be watching the early visible stage of a civilizational phase transition in which some forms of elite cognitive work stop being scarce in the way human societies have always assumed they would remain scarce.

Most of history feels linear while you are living through it. Capabilities improve. Organizations adapt. Industries shift. We narrate it as trend lines. But every so often, a capability crosses a threshold that makes the old equilibrium the wrong frame. The printing press did not just make scribes faster. Industrial machinery did not just make artisans more productive. Networked computing did not just speed up mail. In each case, the structure of the environment changed because a previously scarce capability became abundant or newly scalable.

That may be what we are starting to see now. And cybersecurity just happens to be the place where the evidence has become concrete enough that the old comforting abstractions no longer work.

What Comes Next

The correct response to a quantum moment is not to pretend it isn’t happening. Panic isn’t useful either, ever. Useful is: what does strategy look like in a world where elite cognition begins to become less scarce?

For organizations that build and maintain software, the answer is starting to take shape. The old model emphasized defense-in-depth, patching, mitigations, and response because comprehensive preemptive defect discovery at scale was unrealistic. The emerging model doesn’t eliminate those needs, but it adds something the old model couldn’t offer: a genuinely offensive defensive posture. Continuously applying near-frontier machine reasoning to your own code, your dependencies, your protocols, and your architecture before adversaries do.

The organizations that win in that world will not be the ones with the loudest warnings. They will be the ones that can operationalize machine-discovered advances the fastest. Absorb findings, triage them, verify them, fix them, redesign around them, and repeat at speed. Mozilla’s own account hints at exactly this. They described their team working around the clock, reprioritizing sharply, and focusing relentlessly on exploiting the window of defensive advantage these tools might open.

One caution worth naming: Mozilla included it, and they are right to. If AI makes codebases less human-comprehensible, if the pace of AI-assisted development increases complexity faster than discovery capability can drain it, then the problem may scale alongside the solution. The positive future is not guaranteed. It depends on whether we use these capabilities to compress defect inventories and improve assurance, rather than simply using them to generate more brittle complexity at higher velocity.

And the window is not permanent. Anthropic chose a careful path with Mythos, restricting access, launching Glasswing, trying to give defenders the first move. But Anthropic is one company. AISI’s evaluations cover seven models across multiple labs, and the capability trajectory they describe is consistent and broad. Chinese labs, open-source model families, and frontier developers across multiple countries are advancing on similar trajectories with varying safety philosophies and geopolitical incentives. The strategic argument for hardening platforms now is not primarily about Anthropic’s own timeline for broader release. It is about what is coming from directions that will not share Anthropic’s commitment to giving defenders the first move.

Project Glasswing will not remain exclusive forever. The window is real, but it is not unlimited.

We are at a quantum moment. The thing unsettling this era may also be the thing that makes the next era safer than the one we have now.

Mozilla ran Mythos against Firefox and found 271 vulnerabilities. They patched them all. That is not a catastrophe. That is a proof of concept for something we have never had before: the plausible beginning of truly hardened software. Platforms that have not merely been reviewed and tested in familiar ways, but subjected to sustained scrutiny by machine intelligence operating near the frontier of human capability, and remediated accordingly.

For the first time in a long time, the structural advantage in software security may be shifting toward the people trying to build things that last.

The nearest practical constraint on advancing frontier AI in the United States right now is not algorithmic sophistication, research talent, or even the supply of high-bandwidth memory. It is electricity: the ability to deliver power at data-center scale through a grid that was not built for this kind of demand, across interconnection queues that run to years, and through transmission corridors that require their own multi-year permitting and construction cycles.

The implications for the AI race are pretty significant.

The most immediate consequence is that it changes who can actually compete at the frontier. Frontier model training today requires clusters of tens of thousands of accelerators running at high utilization, and those clusters consume power measured in hundreds of megawatts, sometimes approaching or exceeding a gigawatt for the largest facilities now being planned. Getting that power in the United States is not primarily a capital problem, though it is partly that. It is a time and logistics problem. Major independent system operators carry interconnection queues that, depending on the region and the size of the request, stretch three to seven years. Transmission permitting and construction add further time on top. A hyperscaler that locked in power rights and grid interconnection agreements two or three years ago is in a categorically different position than a well-funded startup trying to do the same thing today. Capital can move fast; grid infrastructure cannot.

Beyond who can compete, the constraint reshapes the geography of frontier AI development in ways that have little to do with talent density or research ecosystems. The question of where to build is increasingly a question of where the electrons are, at what cost, and on what timeline. That logic points to specific regions: ERCOT in Texas, where regulatory structure and existing industrial infrastructure create faster paths to large-load interconnection; the Southeast, where utility load growth has kept those relationships more active; the Pacific Northwest, with its hydropower base; and a growing set of former industrial sites being renegotiated for data-center use. Frontier AI is clustering around power access. The assumption that it is a coastal or university-adjacent phenomenon is eroding under that pressure.

Nor is this constraint symmetric between the United States and other actors in the competition, and China in particular. China’s grid expansion over the past decade has been extensive, and its regulatory structure permits the kind of coordinated, state-directed co-location of power capacity and compute infrastructure that American institutions do not easily allow. The U.S. regulatory environment for large-scale power and transmission involves state utility commissions, FERC interconnection rules, environmental review processes, and permitting authority fragmented across federal and state agencies. That system was not designed for rapid industrial buildout, and it shows. China faces its own constraints, especially on the hardware side, where access to advanced accelerators and high-bandwidth memory remains complicated by export controls. But the ability to deploy utility-scale power for compute on an accelerated timeline is probably easier to achieve under a centralized industrial policy than under the American system, and that is a real asymmetry in the competitive picture.

Other constraints deserve acknowledgment. High-bandwidth memory supply, dominated by Samsung, SK Hynix, and Micron, is a genuine secondary bottleneck, and the pace at which HBM production capacity expands is a real variable in how quickly compute clusters can scale. Continued software and algorithmic efficiency improvements are also meaningful: gains in training and inference efficiency compress the compute required to achieve a given level of capability, which changes the power arithmetic somewhat. Neither of these is trivial. But HBM supply is more tractable than grid infrastructure, because semiconductor fabs can expand on timelines measured in months to a few years rather than the multi-year permitting and construction cycles that characterize transmission buildout. And algorithmic efficiency is an incremental offset against a demand trajectory that keeps increasing: the industry’s direction, toward larger models, longer contexts, and more inference at scale, keeps total power demand growing faster than efficiency gains bring it down. Power-constrained compute buildout remains the binding constraint.

What this means in practice is that competitive position at the frontier over the next several years is substantially shaped by decisions that hyperscalers made some years ago. Microsoft’s nuclear agreement with Constellation, Google’s and Amazon’s long-horizon renewable power contracts, Meta’s large-scale data center commitments, these are not just energy procurement decisions. They are structural bets on position at the frontier, and they are paying off in a specific way: the actors who made those commitments have secured access to power that newer entrants cannot replicate quickly regardless of capital availability. The lead time built into grid infrastructure is doing competitive work that no amount of money can easily compress.

For anyone following the AI race primarily through the lens of model benchmarks, research publications, chip supply chains, or talent acquisition, the power question deserves considerably more weight. The frontier is not running into a software ceiling or a hardware shortage in the conventional sense. It is running into the physical limits of infrastructure that was built for a different era of demand, under a regulatory structure that was not designed to move at the pace this technology now requires. The actors who recognized that earliest and moved accordingly have locked in advantages that are not easy to replicate. That is where a significant part of the competitive dynamic now lives.

On April 19 in Beijing, a humanoid robot called Lightning, built by Chinese smartphone maker Honor (think about that, a smartphone maker… not an aerospace, robotics, or defense company) ran a half marathon in 50 minutes and 26 seconds. The human world record, held by Ugandan runner Jacob Kiplimo, is 57 minutes and 20 seconds. Lightning beat it by nearly seven minutes, self-navigating a 21-kilometer course while adjusting its gait and balance in real time, swinging short forearms for balance, and showing no sign of fatigue as it crossed the finish line.

That is remarkable, and it’s also the wrong frame for thinking about what happened.

One year earlier, the inaugural Beijing robot half marathon was run on the same course. The winning robot finished in 2 hours, 40 minutes, and 42 seconds. The improvement from that time to Lightning’s performance is not the kind of thing you can describe as just iterative progress. In twelve months, a robot went from finishing a half marathon in roughly the time a reasonably fit recreational human might manage, to finishing it faster than any human ever has. The number of competing teams grew from around 20 to 112, with over 300 robots on the course alongside 12,000 human runners. Honor took the top three places in the autonomous navigation category (again, a smartphone company). These are not prototype demonstrations or research results presented at conferences. They are public, filmed, timestamped performances by production hardware, run in front of international press.

The broader industrial context behind the spectacle is what really has me thinking. China currently accounts for approximately 90 percent of global humanoid robot shipments. It has more than 140 humanoid robot manufacturers and over 330 models fielded in the past year alone. Agibot, one of the leading Chinese manufacturers, produced its 10,000th humanoid unit in late March 2026, with half of those units coming in the preceding three months. Unitree’s G1 model sells for around $16,000, which is less than many used cars. The Chinese government has backed all of this with an $8.2 billion national AI investment fund, explicit mention of “embodied intelligence” in its most authoritative national policy document for the first time, and a draft 15-year plan that names robotics as a strategic development priority through 2035.

The United States is not entirely absent from this competition. Boston Dynamics’ Atlas is in commercial production. Tesla Optimus and Figure AI’s Figure 03 are deploying in some factory settings. By most assessments, the US still leads overall in AI software sophistication and the higher-end robotics design. But there is a general assumption/belief running through American technology and policy conversations which perpetuates a myth that China can manufacture at scale but cannot innovate at the frontier, that it copies rather than leads, and that when it comes to genuine capability the gap still runs in our direction.

The Beijing half marathon highlights a concrete, public, unambiguous data point: a Chinese consumer electronics company fielded a humanoid robot that outperformed every human who has ever lived at a specific physical task, did so autonomously with real-time adaptive control, and improved its performance by more than 200 percent year over year. Lightning’s specifications are not secret. The robot is 169 centimeters tall, has leg proportions designed to mirror elite human runners, generates 400 newton-meters of peak torque, and uses a proprietary liquid cooling system developed largely in-house. Its manufacturer claims it runs 14 percent faster than Boston Dynamics’ Atlas. That claim may be contested, but it was made publicly and without rebuttal at an event with global coverage.

The question this raises is not whether the US should panic about robots running fast. It is whether the assumptions we use thinking about the competitive landscape are accurate. The history of technology competition is full of moments where the comfortable consensus was that a rival could not quite do the thing, right up until the thing was done. The gap between “they can’t innovate” and “they just beat the human world record” closed in one year in Beijing, in public, on a course that 12,000 people ran alongside.

Without doubt, the race was useful theater. What this particular performance made visible is an acceleration in physical AI capability, at a manufacturing scale and cost structure the US does not currently match, in a domain that matters well beyond athletics.

The rest of the world is taking AI, AI dominance, and societal integration very seriously… and moving forward fast.

American writer Robert A. Heinlein published a short story in 1941 called “—We Also Walk Dogs,” and if you’ve not read it, it holds up better than most science fiction from that era, which is saying something.

The basic setup is that there is a company called General Services, whose whole value proposition is that they will do anything. Any task, any scale, any complexity. The company’s advertising tagline, and the joke that gives the story its title, is that they handle civilization-scale problems and they also, yes, walk your dog. No job too large. No job too small. One company, everything (you can probably already guess this is really about AI and agentic agents :-) ).

In this particular story, they get hired to host an interplanetary diplomatic conference, which sounds manageable until someone points out that the attendees come from planets with different gravitational fields and cannot all physically sit in the same room without an antigravity system, which does not exist. General Services doesn’t solve this problem themselves. What they do is find the one physicist alive who could theoretically figure out antigravity, a difficult and stubborn man named O’Neill, and then spend considerable effort determining what O’Neill actually wants, which turns out not to be money or recognition. He wants a specific piece of Chinese porcelain, “The Flower of Forgetfulness,” which is sitting in a museum. They get him the bowl. He invents antigravity. The conference happens. Then General Services asserts ownership of the antigravity patent, because O’Neill was on salary at the time, so legally everything he produced while under contract belongs to them (wow, that’s familiar).

I’ve been thinking about this story lately.

What Heinlein drew in 1941 as a clever business concept is more or less what we are actually building right now. Not the individual AI models, but the layer above them, the agentic systems, the orchestration frameworks, all the infrastructure that accepts a problem, routes it somewhere capable, and returns an answer. The whole proposition of that technology is General Services: no domain expertise required, just the ability to assess what needs doing and find the right resource to do it.

The part of the story that felt most like science fiction when I was younger, and now barely feels fictional at all, is the indifference to category. General Services does not have a dog-walking division and a physics division and a diplomacy division. It has a general capability for getting things done, and it deploys that capability wherever the problem shows up. Foundation models work the same way, and it is still a little strange to think about: the system does not know it is switching between tasks, because from its perspective there are no separate tasks, just tokens. Hmmm….. “Mythos”?

The O’Neill angle is the one I find most interesting, though, maybe because it is the least technical. The man who can solve your civilization-scale problem does not want what you are offering. He wants a bowl. And the move is not to offer him more money or a better title, it is to figure out which bowl, and then go get it. That is a large part of the the story, and I suspect it is most of the actual work in deploying these systems too: not the capability itself, but understanding what the person on the other end of it actually needs, which is frequently not what they said they needed.

The patent stuff is probably the part that aged least gracefully, or most gracefully, depending on how you look at it. In 1941 it reads as a clever twist. Now it reads as a fairly live question: who owns what comes out of these systems, and under what terms? The answer is not obvious, and the entities best positioned to shape it are the companies that control the infrastructure.

Anyway, read the story if you haven’t. It’s short, it’s fun, and Heinlein was clearly thinking about something real. The original 1941 issue is in the Internet Archive if you want to read it here: https://archive.org/details/Astounding_v27n05_1941-07_dtsg0318-LennyS?view=theater#page/n125/mode/2up

The Cognitive Sprawl is exploring the intersection of cognitive security, emerging technologies, and the forces reshaping thought, trust, and perception.

Oliver Whang’s long piece in this morning’s New York Times Magazine, “Why It’s Crucial We Understand What AI Is Telling Us,” is the first mainstream treatment of interpretability research I have read that gets the shape of the problem right. It is worth reading in full. What follows is a set of connections between Whang’s reporting and the…

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The first two installments of this series built the architecture. Post Zero established the governing claim: AI agents are being misclassified as software, and every security control written for deterministic applications fails structurally when applied to a reasoning, stateful, always-on, always-credentialed non-human principal. Part 1 diagnosed why that misclassification produces specific, enumerable blind spots in IAM, PAM, SIEM, and Zero Trust. These are the traditional controls that continue to authenticate, authorize, and log while leaving agents operationally ungoverned. Part 2 specified what a governed non-human principal must actually possess: a durable principal record, an execution-bounded credential chain, an enforceable scope contract, a delegation-preserving attribution chain, and a lifecycle that can suspend and revoke authority without collateral outage.

This installment moves from specification to exploitation.

The central argument is that the first generation of agent compromise is not primarily a model safety story, a jailbreak story, or a content filtering story. It is an identity and authorization story. Once an agent can ingest instructions from untrusted sources while continuing to hold the standing authority of a trusted non-human principal, the attacker does not need a separate credential or a new intrusion path; instead, they acquire the ability to drive an existing authorized execution surface. The decisive security question is not whether the model was manipulated. It is whether the manipulated model remained authorized to act under the same scope, credential chain, and delegation path after the instruction source changed. Most enterprise security architectures are not structured to ask that question at the enforcement layer, which means the governance gap that we previously described as an identity architecture failure becomes, here, the attack surface.

The technical brief attached to this post works through what that means in practice. It maps each of the MITRE ATLAS October 2025 agent-specific techniques against the specific identity component each one exploits, making the case that these are not generic AI risks but precision attacks against the governance absences the software misclassification creates. It develops the argument that prompt injection works because agents evaluate semantics before they re-evaluate authority, and that the correct architectural response is instruction-source authentication and scope re-evaluation at the pre-execution gate, not improved content analysis of what instructions say. It addresses memory manipulation and context poisoning as persistence mechanisms against governed identity; a framing that changes the containment architecture required, because the controls adequate for bounding session-scope attacks are not adequate when one successful compromise can alter the starting conditions of every future run. It traces how the OBO delegation chain becomes a privilege escalation path when scope intersection is not enforced at each hop. And it puts all of that in the context of what CrowdStrike’s 2026 Global Threat Report documents as the current adversary operating tempo: organizations relying on human triage inside that window are building detection capabilities that the breakout timeline has already made insufficient.

Three attack scenarios drawn from documented adversary tradecraft show how these mechanisms combine. A scored adversarial exposure assessment gives security and platform leaders a structured way to evaluate their current posture against the specific attack surfaces this brief documents.

The full brief is attached here...

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They ask whether the model can be manipulated; whether prompts can be filtered; whether outputs can be monitored.

Those questions matter, sure, but they’re downstream from the real issue.

The deeper problem is once an AI agent can receive untrusted instructions while retaining the ful…

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