“The perfect fog of war” - October 1, 2026 A study of AI agents, the shift from skills to plugins, autonomous finance, crypto and tokenization, military realignment, and the changing architecture of attention. The old conception of the fog of war depended on deprivation. A system does not need to make something invisible if it can make everything surrounding it irresistible. The fog of war is no longer necessarily the absence of information. It is the perfection of attention. I have been marking versions of this condition for years, usually before I understood what I was marking. The crossing of the swords, anthracite, the beast and the recurring nine-day intervals belong to that record because they preserve an earlier state of observation before whatever followed could reorganize the memory of what came before it. Some later appeared significant and some led nowhere. Both belong in the archive, because a pattern that survives only after its failures have been removed is not a pattern. It is editing. April 2026 matters because much of what surrounded the experience can now be separated from recollection. On April 8, after thirty-eight days of war and the beginning of a fragile ceasefire with Iran, senior American military officials said U.S. forces remained positioned to resume combat if diplomacy failed, with more than fifty thousand American troops still in the region. On April 13, after negotiations failed to produce a settlement, the United States began a naval blockade of Iranian ports. Iran temporarily reopened the Strait of Hormuz on April 17 while warning that it could close the passage again, and on April 22 Iranian forces seized two commercial ships attempting to leave the Gulf. The word ceasefire therefore described a period that also contained military readiness, blockade, disrupted maritime movement, negotiation and the possibility of renewed combat. At almost exactly the same time, another field was becoming unusually easy to follow. Coachella occupied the cultural foreground across April 10 through 12 and April 17 through 19. On April 10, OpenAI was publicly teaching skills as reusable workflows, packages of instructions and supporting material that allowed a method to persist instead of being rebuilt prompt by prompt. During the second half of the month, skills repositories, coding agents, context systems and increasingly autonomous workflows spread rapidly across the technological foreground, while Karpathy’s late-April discussion of the movement from vibe coding toward agentic engineering supplied a name for a change already underway. That technological wave was not something my feed invented. It existed outside me, but the feed made it feel nearly complete. The asymmetry is what stayed with me. The geopolitical field required constant reconstruction because the significance of each development depended upon what happened next. The technological field unfolded with far less resistance. A discussion of reusable skills opened into coding agents, which opened into memory, context, verification, delegation and autonomy, and each of those led naturally into another subject I already wanted to understand. The material held attention because it was consequential enough to justify the next hour.

I’ve been thinking a lot about what happened on September 3, 2026, when so many of the major AI companies went down at roughly the same time. Then came the outage, followed by the outrage cycle. I think I’ve finally figured out what happened.

Apparently, I am spam. I’ve been detected by X’s automated systems as potentially containing spam, so my posts are hidden from recommendations to non-followers, since, well, long before this feature came live, though there’s no historicity to it. I took a look under the hood, where you can check your visibility and shadow-ban status: Anyway, I always like confirmations. x.com/i/under_the_hood

A prompt share and a little self-knowing. Sooooo I wanted to know something. How much do I actually read? It’s also a kind of AI screen-time measure. I’m reading roughly one book’s worth of AI-generated text every week, about 50–70 standard books a year, requiring around 5–7 hours of reading in a typical week. That works out to roughly 72,000–104,000 words of AI output every week, about 10,000–15,000 words per day, or 37–54 book pages per day at 275 words per page. So I wondered, if I examined my entire AI chat history from my very first session until now, and assumed I actually read 100% of every response the models gave me, how much am I really reading every week? Not tokens. Actual words, pages, books, and hours of reading. I am turning AI usage from an app-engagement metric into an information-consumption metric. “Hours spent with AI” and “words actually read” describe two very different things. So I wrote this prompt: AUDIT full accessible AI-chat history, first session → now. ASSUME read_rate = 100% of assistant/model outputs. MEASURE wherever possible. Estimate only where necessary. RETURN: • total_output_words • avg_words/week • avg_read_time/week @ {200, 250, 300} wpm • avg_books/week @ 80k words/book • avg_pages/week @ 275 words/page • lifetime {words, hours, books, pages} • rolling {30d, 90d, 365d} vs lifetime baseline • accessible_history_% + confidence_range Do not infer whole-history volume from a small sample when broader records exist. Label every result: {MEASURED | ESTIMATED | UNKNOWN} FINAL: “Average weekly AI reading load ≈ X–Y hours, equivalent to A–B standard books.” I think this is a much more interesting way to measure AI use than messages sent or hours spent in an app. How much have you actually read?