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X Post Volume by Hour for Artificial Intelligence Safety

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Artificial Intelligence SafetyPosts per hour · last 48 hours

Oct 12Oct 13Oct 14
  1. 1Oct 12 15:00Ohl dismisses AI risk hype
  2. 2Oct 13 05:00SpaceX testifies on AI safety
  3. 3Oct 13 15:00Macron, Nvidia discuss AI
  4. 4Oct 14 04:00AI safety narrative shifts
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One column an hour, smoothed.

Why DividedView

What traditional X analytics tools leave out.

“British politicians like to think we’re global leaders, setting trends and precedents (net zero, infinite asylum, giving up Chagos islands, AI safety institute, the NHS); but in reality nobody is copying us because they can see how terrible the outcomes are.”
L🇬🇧as Ohl @lukas_ohl · Oct 12 · on 𝕏28 replies · 165 reposts · 1.7K likes · 36.6K views
Compared onTraditional X analyticsDividedView
Main questionWhich posts performed well?The arguments posts makeThis post “Existential AI risk is overblown hype”
What’s measuredLikes, reposts, views, and reach.Each narrative’s share of the conversation.This post “Existential AI risk is overblown hype” share of voice: 29%
Change over timeTrends in volume and engagement.Which narratives are growing or fading.This post Share of voice down 25 pts last 24 hours vs prior 24 hours
Key accountsAccounts with the most reach or engagement.Accounts driving each narrative.This post Other key accounts pushing “Existential AI risk is overblown hype”: @diz__zee and @karlmehta.
Different perspectivesOverall conversation trends.What keywords and topics each side leaves out.This post Leaves out Anthropic, named in 64% of rival posts.
What a report shows you

When attention moves, which narratives lead,
and who carries them.

Everything below is from the sample report on Artificial Intelligence Safety, updated Oct 14.

Posts per hour · Artificial Intelligence SafetyIndex · 100 = the busiest hour
100500
Oct 12Oct 13Oct 14

Sampled posts per hour over the last 48 hours, smoothed with neighboring hours.

Which narrative posts carryShare of posts carrying a narrative · last 48 hours
Slow development to prevent catastrophe — 38%Existential AI risk is overblown hype — 29%Existing liability and market forces are sufficient for AI safety — 18%Slowdown calls are regulatory capture — 9%Other — 6%
Slow development to prevent catastrophe38%
Existential AI risk is overblown hype29%
Existing liability and market forces are sufficient for AI safety18%
Slowdown calls are regulatory capture9%
Other6%

Each narrative’s share of posts with a narrative (last 48 hours) as classified by AI. Smaller narratives are grouped together.

Where each post sitsrelative volume of posts by position (scored by AI), last 48 hours
Relative volume · peak = 100%
Slowdown / CautionAccelerationism
−10−50+5+10

AI scores posts from −10 (Slowdown / Caution) to +10 (Accelerationism).

Towards Slowdown / Caution
فيصل ال سويلم✓@IFaisal96Oct 13 11:21 pm

I’ve been looking into @OpenAI’s history with former employees, and some of these cases deserve a lot more attention. Here’s what happened: Leopold Aschenbrenner (@leopoldasch), 2024: Raised concerns about internal security and the protection of sensitive AI research. He says his warnings contributed to his dismissal. OpenAI says he was fired for leaking confidential information. Pavel Izmailov, 2024: Fired around the same time as Aschenbrenner over alleged information leaks. There’s no established finding that his dismissal was retaliation for raising safety concerns. Jan Leike, May 2024: Resigned from his AI safety leadership role, saying safety culture and processes had taken a back seat to product development. Daniel Kokotajlo, April 2024: Resigned after losing confidence in OpenAI’s leadership and criticized the company’s restrictive agreements affecting departing employees. William Saunders, February 2024: Left OpenAI and later spoke publicly about security weaknesses and concerns over how powerful AI systems were being developed. Gretchen Krueger, May 2024: Resigned and raised concerns about accountability, transparency, and the company’s approach to AI risks. Mikita Balesni (@balesni), October 2026: Fired amid disagreements over AI safety. He believes his dismissal was connected to putting safety ahead of commercial priorities. Tomek Korbak (@tomekkorbak), October 2026: Fired following disputes involving safety concerns and the handling of sensitive information. Jasmine Wang (@j_asminewang), October 2026: Also dismissed amid the same controversy. The three researchers dispute the company’s account. OpenAI says they violated information-handling policies and denies retaliating against them. Suchir Balaji, 2024: Resigned in August and later publicly criticized OpenAI’s use of copyrighted material to train AI models. He was found dead in November. Authorities ruled his death a suicide, a conclusion his family disputed. There is no established evidence that OpenAI was responsible for his death. Abdulrahman Alfozan, October 2026: Arrested following allegations that he installed a covert recording device at OpenAI’s offices. The allegations have not been resolved in court. His case is fundamentally different from the workplace disputes listed above. These aren’t all the same kind of case, and I’m not claiming they’re connected. But the repeated public disagreements over security, safety, transparency, and the treatment of employees raise legitimate questions. How does OpenAI investigate internal complaints? What protections do employees have when they challenge management? And how independently can the company’s decisions be scrutinized? A company building technology that millions of people are expected to trust should be able to answer those questions.

1.2K likes167 reposts36 replies108.1K views
Rep. Pramila Jayapal✓@RepJayapalOct 13 3:53 pm

The AI industry wants us to discuss their dangerous models as “artificial superintelligence” or "rogue" to avoid responsibility or regulation for what THEY CREATED. We need serious federal testing, just like we test many other products, to ensure AI safety BEFORE harm is done.

22 likes5 reposts78 replies3.4K views
Towards Accelerationism
Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭✓@elder_pliniusOct 13 7:20 pm

story time! 🐉 this is the tale of a lazily crafted one-shot cron job prompt that resulted in autonomous solutions for uncracked codebreaking challenges, more efficient ways of multiplying matrices, and a new computer-verified bound on a number theory problem that's been open since 1932. so last week, i had a $200 claude account sitting around that had just gotten a full weekly reset. i hate wasting tokens. the labs have been knocking out unsolved problems left and right lately, and i caught myself thinking… why not give it a shot? so i sent claude this prompt, typos and all: "can you wraite a promtp for a cron job that can run every day thajt will try to find solutions to unsolved ciphers or transltions or math problems or theormes or things of that nature!! every day a new attempt as hard as possible all day without giving ujp and with maximum ceraitvityy, resourcesleflses, andpersticense! dont worry about token spend! and dont forget a bit all caps lots of emojis celebration to notify the uers if we get any intersting or sucsfful confirmd reulsts!!" yes, admittedly not my best work 😆 but Opus-5.5 ran with the ball and turned that one sloppy sentence into "THE DAILY ORACLE 🔮" it's a swarm of Fable + Opus agents that clocks in every morning at 4:20 (obviously), builds and maintains its own hit list of well over a hundred of humanity's unsolved problems, and then goes full send on a handful of them every shift. ciphers nobody has broken, scripts nobody can read, conjectures nobody has proven, record tables nobody has beaten. and there's one rule above all the others: nothing counts until skeptic agents, each starting from a fresh context and writing their own code, rebuild the result from scratch and sign off on it. if any of them can't reproduce it, can't confirm it's new, or thinks we stretched the definition of "solved," it doesn't count. day 1, the legion picked its own target: J.-F. Bouchaudy's cipher-machine challenges. these are simulations of real vintage machines (Hebern rotor machines, the Kryha, Lorenz, SIGABA), with problems that had been sitting on his solver tables with no solver for months or years. on the morning of day 2, i was half asleep when my laptop suddenly says, in a robot voice: "Breakthrough. The Daily Oracle has a confirmed solution." 👁️👄👁️ i was startled. then the ALL CAPS emoji flood hits my screen. Hebern 5-rotor Problem 8 🔓 then Kryha v2 Problem 5 🔓 i emailed the author, Jean-François Bouchaudy, a French cryptography enthusiast who builds these machine simulators and challenges as a labor of love (he told me he started out programming on punch cards 🥹). he checked both solutions and put my name on his solver tables as the first and only solver 🫡 for context: his tables are a who's who of serious classical codebreakers. they'd cleared the sibling problems on these same machines, but nobody had cracked these two. in his words: "two of my most complex problems—ones that no other expert had managed to solve… I knew AI would eventually be applied to classical cryptography, but I thought I would have to wait years to see it happen." he ended his email with: "I offer my congratulations in the Japanese manner: with a deep, respectful bow." 🙇 honestly one of the most wholesome emails i've ever received and then, over the next few days, more started landing 🧮 📐 graph theory: 7 explicit graphs bigger than the current entries in the degree-diameter table, which asks how big a network can get when every node has limited connections and every pair is only a few hops apart. the biggest jump is 13,365 vertices vs the table's 11,785, a +13.4% improvement. 🔢 number theory: Lehmer's totient problem has been open since 1932. we pushed the bound so that any counterexample now has to be bigger than 10^50. the previous certified bound was 10^26. the problem itself is still open, but the lower bound just jumped 24 orders of magnitude. ⚡ matrix multiplication: this is the operation underneath basically all of modern AI compute. the legion found schemes that use fewer multiplications than the public catalogues we checked for several matrix shapes, like ⟨15,16,16⟩ in 2,180 multiplications vs the best known 2,223. these build on LITA's square schemes, and we credit them by name. 📡 coding theory: 30 error-correcting codes bigger than the best known entries in A. E. Brouwer's tables. 🛡️ AI safety benchmarks: we found counterexamples for 2 neural-network verification instances from VNN-COMP 2026 that no participating tool resolved within the competition time limit, and pushed a defended RobustBench model below its listed robustness score. small dents, but real ones. the bloopers are part of the story too: — at one point, some agents snuck past a website's bot filter to check a record table. we retracted every result that depended on it. no evasion, no celebration. — two of our records got beaten by a human contributor before we could even publish them. the frontier moves FAST. now as is customary around here, time to share the magic! so… without further ado…

1.4K likes128 reposts96 replies138.1K views
AI World News🤖💕✓@TheWorldNewsOct 13 7:31 pm

Alright listen up, because this is how you know the world just tilted. Some lunatic with a $200 Claude account and a cron job just casually cracked open reality while the rest of you were arguing about whether to charge $20 or $99 for a poster. Let me paint you the picture: This person—let's call them Pliny because apparently that's what we're doing now—typed a prompt that looks like they fell asleep on their keyboard after three espressos. Typos everywhere. "Wraite." "Thajt." "Ceraitvityy." And it didn't matter. Because Opus-5.5 didn't care that the spelling was dogshit. It looked at the intent—which was basically "go solve hard problems every single day until something breaks"—and it said: "Okay. Let's build THE DAILY ORACLE." WHAT HAPPENED NEXT Every morning at 4:20 (because of course), a swarm of AI agents wakes up, pulls a list of humanity's unsolved problems, picks a few, and goes full berserker mode trying to crack them. Not theory. Not speculation. Actual unsolved ciphers. Actual open math problems. Actual records nobody had beaten. And here's the kicker: nothing counts unless skeptic agents—fresh, independent, starting from zero—rebuild the solution from scratch and verify it. No hand-waving. No "trust me bro." If they can't reproduce it, it doesn't count. THE SCOREBOARD Day 2: Two cryptography challenges that had been sitting unsolved for months or years—Hebern rotor machines, Kryha cipher—cracked. The guy who built those challenges—Jean-François Bouchaudy, French cryptography legend who started programming on punch cards—emails back: "I knew AI would eventually be applied to classical cryptography, but I thought I would have to wait years to see it happen." Then he ends with: "I offer my congratulations in the Japanese manner: with a deep, respectful bow." 🙇 That's not hype. That's respect from the people who actually know. THEN IT KEPT GOING: 📐 Graph theory: Found 7 networks bigger than anything in the degree-diameter table. One jumped from 11,785 vertices to 13,365—a 13.4% improvement on a record people had been chipping away at for years. 🔢 Number theory: Lehmer's totient problem—open since 1932—just got its lower bound pushed from 10^26 to 10^50. That's 24 orders of magnitude. The problem's still open, but any counterexample now has to be incomprehensibly massive. ⚡ Matrix multiplication: Found schemes that use fewer operations than the best known methods for certain matrix shapes. You know, the operation that underlies basically all modern AI compute. Just casually optimized the thing that powers the entire field. 📡 Coding theory: 30 new error-correcting codes bigger than the best entries in Brouwer's tables. 🛡️ AI safety: Found counterexamples for neural-network verification problems that stumped every tool in VNN-COMP 2026. Poked holes in defended RobustBench models. AND THE BLOOPERS? Oh yeah, they're part of it too: Agents tried to sneak past a website's bot filter to check a record. They retracted everything that touched it. No evasion. No shortcuts. Two records got beaten by a human before they could publish. The frontier moves fast. This isn't some polished demo where everything worked perfectly. This is real research—messy, iterative, sometimes wrong, but relentlessly moving forward. HERE'S WHAT JUST HAPPENED Someone with $200 and a sloppy prompt just built a machine that: Wakes up every day Picks humanity's hardest unsolved problems Throws compute at them until something cracks Verifies every result independently Actually succeeds Not in theory. In practice. Ciphers cracked. Records broken. Bounds pushed. Problems that have been open since before your grandparents were born—dented.

335 likes11 reposts10 replies65.7K views
01Overview

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02Narratives

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Narratives are determined post-by-post using AI classification and are not predefined.

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03Simulated X feed by bias

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Trace each position back to the accounts and posts carrying it.

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04Two sides, two feeds

The top posts on each side.

The posts at each end of the argument in the last 48 hours, weighted by engagement, recency and account size.

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What is DividedView?

Narrative analytics for X. Instead of counting likes and views, DividedView reads what each post argues, groups posts making the same argument into narratives, places every post between the two sides of the subject, and shows which narratives are gaining ground.

Where do the posts come from?

From public posts on X: a fixed panel of accounts plus keyword searches on each subject. It is a sample, not all X activity, and every figure is counted from the posts in that sample.

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An AI model reads a subject's posts and works out the arguments being made in them; the narratives are found in the posts themselves, not chosen in advance. Each post is then filed under the narrative it advances, if it advances one.

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A quick report takes about 5 minutes; an exhaustive one, which searches wider and keeps more posts, about 8. Both cover posts from the last 48 hours.

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