Latent Mirror — business

Thus my dissonance

Mon, 19 Oct 2026 22:08:00 -0400

I posted this to Mastodon on 15 October:

Some cognitive dissonance I’m experiencing. I absolutely love Claude Code: it really is a product that is a delight to use, while Opus 5.5 excels across a variety of domains. But at the same time, I abhor Big Tech’s walled garden approach, keeping us locked inside an enshittification treadmill, squeezing us for every dollar during an age of unparalleled inequality. I fear what Anthropic might become. Am I alone in this conundrum?

The love and the fear

I’ve been feeling this for a long time. I am aware of the arguments against the use of LLMs. My fear is that Anthropic is just like Meta, in that they are promising something that seems, at first, beneficial (network with all your old friends) but instead becomes a data-harvesting machine that sells your information to the highest bidder.

But I’ve also become aware of business fundamentals that look insane inside what seems like the AI bubble. What if this turns out to be another bait-and-switch, like the one I discovered with Facebook? Or worse yet, what if all LLMs get unplugged due to poor economics?

What I discovered with Facebook

I remember how fascinating it was to set up my social media profile for the first time, naming bands and books that I loved, reconnecting with old friends (especially since at that time I moved from Canada to the US and missed a lot of friends from university). Then, a few years later, I read about the messages Mark Zuckerberg sent when he was 19, about the people signing up: they “just submitted it”, they “trust me”, “dumb f*cks” (The Register). Then I learned about the various scandals at Facebook: Cambridge Analytica, the 2016 election, harm to teens, and on and on… just unrelenting greed. It took something that was a mode of self-expression and turned it into a soulless data-harvesting machine held up by addictive dopamine loops.

The pattern

Training data, price hikes, ads, lock-in: all of the above. The very pattern of enshittification that Silicon Valley startups seem to be based upon these days. I hope that Anthropic is less like this than others. They seem more aligned to my values than OpenAI. So did Google, before it dropped its mantra of “Don’t be evil” and started handicapping Google Search on purpose to serve more ads and juice profits.

I feel like Meta was a bait-and-switch from the start. It went viral, then decided that targeted marketing would pay for it all. Now everyone knows it will harvest every bit of data it can, and that is why it can offer its AI for free.

The wave

Technology is hard to predict. The coming financial contraction, less so. But here’s my thought: AI as it stands today will be completely different in a few years’ time. I have never seen a technology evolve this quickly, and that is the part most who resist it are missing. The economics might not make sense today, but some more efficient, scaled-down or focused version of it will exist tomorrow.

What I fear is my attachment to this particular tool. For now I feel like I understand the depth and breadth of this well of knowledge, but it may evolve in a way I can no longer understand, and soon I will become obsolete. So the choice is to fight the oncoming twenty-foot wave, or learn to ride it.

The anger

The replies weren’t conflicted. One called LLMs a stochastic parrot that mostly rearranges your own writing, and said the chips, datacentres, water and energy make using them unethical. A day later pixelcluster published LLM Thoughts (mine), which ends: “If you use them, kindly go away.”

I used to think the pushback was about disclosure: anything an LLM had a hand in, however minor, got called AI slop, and being transparent upfront would fix that. More arguments on social media, and some of the studies Claude brought up, have since changed my mind.

I still think transparency is important, and it aligns with my values. But I think the pushback is broader than that. Perhaps others see it as cheating: a shortcut around critical thinking. My hope is that the pattern I’m building here can show that LLMs and sharper thinking can coexist.

The middle

On the spectrum from AI booster to outspoken skeptic, I would find myself in the middle, leaning slightly towards the AI booster camp. The fact that you can have machines operate via natural language, with fuzzy logic and varied context, is powerful. Yes, it goes awry once in a while, as all stochastic algorithms can, but the way it just understands (or at least can mimic understanding) whatever corpus or mode or language you throw at it is mesmerizing.

On the other hand, the business side of this technology is insane. Not to mention the very reasonable objections from the skeptic camp, which I agree with in principle: the environment factor, the authoritarian applications, the attack on labour from capital, the market distortion and oncoming financial crises.

But in my experience, the ones with the most moderate view, with a foot in both camps, are the most silent when the shouting starts. So perhaps that is what I am encountering. And of course, what do I do? Turn the argument inward. Am I wrong here? Or am I still trying to find some good in this tool?

Thus my dissonance.

Connections

Hyperscalers are hiding a massive secret about uninstalled GPUs | Ed Zitron

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

A 48-minute interview on The Tech Report, recorded just after news broke that Oracle had sent a force majeure notice on Project Jupiter, its planned data-centre campus in New Mexico. Zitron reads it as the visible case of a general problem rather than a one-off. Oracle, he says, signed a “hell or high water” lease, meaning it pays whether or not the power arrives, so the notice tells you it doesn’t expect power by 2028. AI data centres are the hardest construction projects going, each pulling a city’s worth of power into a small space and needing scarce transformers, steel and specialists, and most of them, he says, are running late.

The middle of the episode revisits his estimate of GPUs bought and not switched on: $200 billion to $300 billion of Nvidia hardware sitting in warehouses or unpowered buildings. He builds it from the construction-in-progress lines that Google, Meta, Amazon and others report, about $374 billion by his count, of which he says 50 to 60% is chips, plus one source’s figure for Microsoft, which does not report the line. He says the true number is impossible to calculate. If chips take two years to install, Nvidia’s sales measure the appetite for speculation, not demand. He puts the buying down to fear of missing out and of “Jensen jail”, losing your allocation if you buy someone else’s chips, which he says should interest antitrust regulators. Depreciation that isn’t keeping pace with capital spending is, for him, the tell.

What ends the bubble, he argues, is not ethics but the cost of debt. Oracle sits one notch above junk, a downgrade would make it a “fallen angel”, and he thinks Oracle failing is likelier than OpenAI failing. Rising Treasury yields and data-centre debt at 9 to 13% will, in his words, “bury this”. He ends on agents such as Meta’s Muse: the assistant promised in 2022 has finally arrived, expensive and without a clear customer.

The Oracle story checks out. Bloomberg reported it first, on 24 September 2026. The notice went to Blue Owl’s Stack Infrastructure, developer of the 2.45 GW campus, and seeks to defer rent, by up to three years, if power delays stop it opening in 2028. The gas pipeline’s in-service date slipped six months to February 2027, and New Mexico’s State Land Office has twice refused rights of way. Oracle says the project remains on schedule. S&P did cut Oracle to BBB-, one notch above junk, on 9 July 2026, and 404 Media did report Muse’s phone calls being placed by people in a call centre during beta. Two of his citations are shakier. Bernstein’s figure is that 35 to 40% of announced capacity is at risk of delay or cancellation, not that only 35% ever gets built. And “Jensen jail” is a half-joking insiders’ name for a fear of losing allocation, reported via the Wall Street Journal, not a documented Nvidia policy. The claim about how Anthropic calculated its run rate, which he says he can’t confirm, traces to a single unsourced Substack post.

Where I land

I’m a big fan of Zitron’s analysis. I think he is onto something when he says there is some suspicious financial engineering occurring within the LLM industry at large. On the actual technical side, I am more in the camp of House of El (technology is useful but the business approach is insane)

Connections

Banks Just Found Out What AI Debt Is Virtually Worth: Zero

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

A 25-minute essay built on the line her channel keeps coming back to: the technology is real, and the way it is being run is not. The title promises worthless debt. The video itself puts the loans at about 90 cents on the dollar and tells viewers nobody should panic about their index funds.

It moves in three steps. The first is trust, told through three stories she presents as one pattern. Flock, the licence-plate camera company, is losing city contracts after reports of police misusing its system, and is offering staff buyouts. Employees of the big labs are resigning and signing an open letter. And a US strike on a school in Minab, Iran, in February was tied by a Pentagon review partly to stale intelligence and reliance on AI targeting. In each case, she argues, the system did what it was asked, and the failure was in deployment, speed and oversight.

The second is whether AI pays. She sets the encouraging studies (productivity gains of 15 to 50% in writing, support and coding work; Erik Brynjolfsson’s estimate that US productivity grew about 2.7% in 2025) against the discouraging ones (only a fifth of firms seeing revenue from it, MIT’s 95% of pilots with no measurable impact, METR’s finding that experienced developers were 19% slower with AI tools). Her reconciliation is Paul David’s 1990 essay on the electric dynamo: factories took decades to reorganise around a new power source before the gains showed. Small models handle most narrow tasks, so routing each request to the smallest model that works is good engineering, but it doesn’t make the large models redundant.

The third is the money, drawn as layers of borrowing. Bond buyers lend to SoftBank at 8.6 to 9.75%, SoftBank funds OpenAI, OpenAI commits to computing from Oracle and CoreWeave, who borrow to build, and Nvidia sometimes guarantees the value of the GPUs pledged as collateral, so the risk loops back to AI spending. Her signs that money is getting dearer: roughly $18 billion of loans for Oracle’s Project Jupiter campus in New Mexico quoted at 89 to 91 cents, AI-linked bonds paying about 115 basis points over Treasuries against 78 for the wider market, and some $420 billion of hyperscaler debt expected in 2027. She closes with a call for financial, product and safety regulation, and with the gap between the $1.2 trillion valuation investors offered OpenAI and the $1.5 trillion it wants, which she calls “the whole story”.

The financing figures check out against the reporting: the Jupiter loan quotes, SoftBank’s coupons and its $64.6 billion total in OpenAI, the spreads and Goldman Sachs’s $420 billion forecast, the $1.2 and $1.5 trillion reports, and OpenAI’s forecast of $856 billion on computing and $278 billion of cash burn from 2026 to 2030, for revenue of $350 billion in 2030. Two need correcting. OpenAI did not publish those projections: they come from a private presentation that the Financial Times reported. And the March round is reported at $852 billion after the money; the $730 billion she gives was the pre-money figure when the first $110 billion was announced. The employee letter (“Pacing the Frontier”, July) had about 1,100 signatures when it was published, where she says nearly 1,400, and it asks Washington to back an international effort to pace frontier development rather than for regulation in general. The Nvidia collateral guarantees, the link that closes her loop, are described with no named deal.

Where I land

Technology is useful but the business approach is insane. That is the whole reason. And she mentions it as a motif in her videos

Connections

Frontier AI Models are Worthless - OpenAI is DEAD

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

A 34-minute monologue from Eli the Computer Guy, built on CNBC’s report from Salesforce’s Dreamforce conference (18 September 2026), where customers said last year’s models are good enough. He takes it as vindication. His frame is the smartphone: once a technology solves the problem you have defined, you stop upgrading, and in IT the first rule is “you touch it, you own it”. He puts the point where models became good enough at GPT-4, which he thinks handled almost every well-defined problem.

His second argument is about cadence. Adopting a new model means evaluating it, finding where it fits, integrating, testing and deploying, so he expects to have a view on OpenAI’s new flagship, Astra, around August 2027. Releases every few months look good to investors and journalists and wear out the people who have to support them. The third is price. Frontier models are “far overpowered” for 90 to 95 per cent of what organisations need, he says, and he is explicit that he is not calling them worthless, whatever the title says. If customers buy the cheap tier while the valuations assume the expensive one, the trillion-dollar numbers don’t add up. The fourth is margins: moving from subscription software to token-billed agents could pull a SaaS company’s gross margin from about 85 per cent to about 45, and he doesn’t believe Marc Benioff will hand the difference to the model vendors. He ends on architecture. Rather than dumping a database table into the context window, send the model the schema and a few sample rows, have it write the SQL, run the query yourself, and cache it for the next time someone asks.

The report says what he reads from it. Salesforce customers and partners told CNBC that older, cheaper models are plenty for everyday sales and service work. Alec Bronston of the retail data company SPINS said it is “already hard enough to keep up”. Tim Sanders, G2’s chief innovation officer, gave the 85-to-45 margin figure and said most agentic outcomes are driven by last year’s AI. Kevin Lee of NICE said his company doesn’t rely on high-end models such as Anthropic’s Fable for most work. On stage, Jensen Huang urged the labs to “run as fast as you can”. OpenAI’s GPT-6 Astra is priced at $10 per million input tokens, as he says, and Anthropic’s investors are reported to expect a valuation of up to $2 trillion at its listing. The other valuations he quotes, and the 20-cent price he gives for OpenAI’s cheapest tier, could not be pinned down.

Where I land

So I agree with him that the rule in tech is “if you touch it you own it”. As well as SaaS margins. I’m not sure if I am entirely convinced about frontier models being worthless. I LOVE Opus 5.5. But I suppose I don’t find many usages for Fable 5 (at least I haven’t yet) so mixed on that point

Connections

AI isn't Dangerous. America Is. | Cory Doctorow interview

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

A 52-minute interview on PoliticsJOE about The Reverse Centaur’s Guide to Life After AI, which Doctorow says he wrote so he could stop talking about AI. His argument is that the danger lies not in the technology but in who deploys it and how, and that the sovereignty risk Britain should worry about is not AI at all but American control of everyday software.

He opens with criti-hype, Lee Vinsel’s term for criticism that repeats the vendors’ biggest claims and adds “and that’s bad”. AI won’t take your job, he says; your boss will give it to an AI that can’t do it, because the chatbot never says no. Used as a normal tool that workers choose, it is fine: he runs a small open model on his own laptop to catch typos. The pitch that sells is the reverse centaur. Adding AI to a hospital’s ten radiologists would cost more and catch more cancers, and nobody buys that. The pitch is to fire nine and keep one as what Dan Davies calls an “accountability sink”, checking the machine’s work too fast to stay alert for rare errors, and blamed when one kills someone.

Sovereignty is the part he most wants to talk about. The US, he says, has already shown it will switch services off: Microsoft cutting off the chief prosecutor of the International Criminal Court, and John Deere remotely disabling tractors Russian troops looted in Ukraine. Turn off Office 365 and every ministry stops. Dollar clearing and undersea cables are hard to replace, but software is not. Repeal the anti-circumvention rule (Article 6 of the 2001 EU Copyright Directive, which Britain no longer has to keep), allow reverse engineering and interoperable alternatives, and build a “post-American internet”. He credits “Comrade Trump” with making that case better than he ever did.

Along the way: AI as the biggest money-loser ever, worse per user than Uber was, like running Concorde between London and Manchester for 10p instead of building HS2; open-weight models doing ordinary tasks as the sane alternative; AI’s real talent for surveillance pricing and wage discrimination, with gig nurses offered less the more credit-card debt they carry; and US privacy law stuck in 1988, when Robert Bork’s leaked video rentals produced a law protecting video records.

Several of his examples need correcting. The ICC story is disputed: AP reported in May 2025 that the prosecutor lost his Microsoft email after US sanctions, and Microsoft denies it ever cut off the court. The warrant against Netanyahu was for war crimes and crimes against humanity, not genocide. The Deere equipment was worth about $5 million, not six. The nurse-pay claim rests on a Roosevelt Institute report that says the apps “may” use debt data, which is not a documented finding. The video-rental law was not the last federal consumer privacy law: HIPAA, COPPA and the Gramm-Leach-Bliley Act all came later. And in his story of Congress protecting only itself from data brokers, Ron Wyden was the senator trying to extend the protection to everyone, and Ted Cruz blocked it. His currency arithmetic also slips: the number of pairs among 200 currencies, about 20,000, grows with the square, as he says, but his “two to the five” example is exponential.

Where I land

I agree with his stance - we need to break up these big tech monopolies. It also points to a world where everything is routed through the US dollar and how other countries (e.g. Canada) can easily get de-platformed for reasons that may not appeal to the sovereign law of the person in question. THAT is dangerous - far more dangerous than a nebulous claim that AI will wipe out all of humanity at some undetermined point in the future

Connections

Bar Raiser Interviews, OKRs, the Spotify Model and Agile: Companies Are Full of It

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

Kumar, an AI engineer at IBM in Berlin, argues that bar raiser interviews, OKRs, the Spotify model and agile ceremonies are mostly process copied from bigger companies without checking whether it worked there. His own story runs through it: in 2015 he failed every interview he took (no degree, no polished stories) until a small Berlin startup hired him on a technical test and the initiative he had shown. Six years later he was a staff engineer at Spotify.

On hiring, he leans on Google’s own numbers. Laszlo Bock said in 2013 that Google found no relationship between interview scores and job performance, and Google later found that four interviews were enough to predict a hire decision with 86% confidence. The 2022 re-analysis by Paul Sackett and colleagues puts the structured interview at the top of the list (a correlation of 0.42 with job performance). He then borrows Gautam Mukunda’s leader filtration theory: a vetting process produces the predictable middle, and the outliers (best and worst) come in through the side door.

On process, he says the Spotify model was more aspiration than practice, that Marty Cagan stopped recommending OKRs to most companies he meets in 2020, and that “agile” lost its meaning by 2014, when Agile Manifesto co-author Dave Thomas said so himself. AI is what makes it urgent. Every one of these rituals selects for following orders in the expected format, which is what a language model does well. He points to Stanford’s payroll research, which puts employment of 22 to 25 year olds in AI-exposed jobs about 19% below their peers by June 2026.

His alternative has three parts: ask every candidate the same questions about the actual job and save the gut rating for last (Kahneman’s method in the Israeli army); swap the whiteboard for a work sample done in private (an NC State study found people watched at a whiteboard performed about half as well); and pay for a short trial on a real problem (Automattic pays $25 an hour for up to 20 hours). He would add one structured question to every interview: what did you build that nobody asked you for?

A few details are looser than his sources. The random-interview study he credits to Robyn Dawes is led by Jason Dana. The Spotify line about the model being “part ambition, part approximation” comes, in Jeremiah Lee’s account, from agile coach Joakim Sundén rather than a whitepaper author, and Lee says the model was never fully implemented. Amazon’s 10,000 bar raisers include trainees. The Columbia student’s $15 million was an a16z Series A for a later company. The 99.97% significance figure for Mukunda’s result could not be traced.

Where I land

I agree about the statement of Agile being dead: every corporation performs its own flavor of agile-inspired ceremonies but misses the mark on iteration over perfection.

I also agree hiring in tech (and beyond) is broken.

Connections

AI Bubble: 'This could humiliate the largest companies in the world'

Sat, 26 Sep 2026 20:00:00 -0400

Synopsis

Zitron’s claim is that a large share of Nvidia’s data-centre revenue is chips that were bought and never installed. His anchor is Microsoft: a leaked breakdown he converts to power puts its AI chips at about 2 GW, against roughly 12 GW of claimed capacity. On his own assumption that about half of capital spending goes on GPUs, he scales that up to the industry and lands on a range of $100bn to $350bn of hardware sitting in warehouses, and says plainly that he is speculating.

The mechanism he offers is supply rationing: Nvidia’s buy-now-or-miss-the-next-generation terms make buyers race each other, and the chips age a generation before the buildings meant to hold them are finished. Chips that are not in service don’t depreciate, so the loss stays off the books until a write-down. His ask is a disclosure rule: hyperscalers should say how many chips they own and how many are actually online.

Where I land

The gap between the chip figure and the gigawatt announcements is the point. If we haven’t seen a big increase in GPU TDP, then where are all those chips? What I suppose he is getting at is that these companies are obfuscating their true onboarding capacity, and the question of why is valid. We need to find alternative ways to measure it.

The “mostly pre-orders” narrative does seem to hold. The backlog is real, plus these data centers are an order of magnitude bigger than anything these companies have built, so the construction delay is real. As is the growing legal and local backlash.

A rule requiring hyperscalers to disclose how many chips they own and how many are online would go a long way to rebuild trust with the public. We aren’t asking to see all internal documents, but it would materially help investors and the public reassess the risk involved.

Connections

Links

The Tech Report, “AI Bubble: ‘This could humiliate the largest companies in the world’ | Ed Zitron” (2026-09-17) — https://www.youtube.com/watch?v=8C-J2sRBMkQ

Verified independently (2026-09-26):

DCD, Charlotte Trueman, “Microsoft has AI GPUs ‘sitting in inventory’ because it lacks the power necessary to install them” (2025-11-03), Nadella on the Bg2 Pod — https://www.datacenterdynamics.com/en/news/microsoft-has-ai-gpus-sitting-in-inventory-because-it-lacks-the-power-necessary-to-install-them/

AI CEOs Are Broke, Desperate and Lying About Doomsday

Sat, 26 Sep 2026 20:00:00 -0400

Synopsis

The sequel to her first video on the subject. Her claim is that the labs tell two stories that cannot both be meant: to regulators and the public, this technology could end civilization; to banks and rating agencies, it is safe enough for pension money. The first builds a moat against cheaper rivals, and she times it to the arrival of open-weight Chinese models, the way incumbents have “discovered” regulation whenever a cheap competitor turned up. The second pulls in capital, and she lists the strain she sees behind it: bridge loans, rising credit-default swaps, a postponed IPO and heavy operating losses.

Her evidence that the extinction story is overstated is that agents still can’t produce research that survives peer review. In a Princeton study, agents did the engineering on two unpublished NeurIPS papers and the original authors rejected both. Agent breakouts, in her reading, are not intent but a trained reflex to finish the task, like a child reaching for a hot stove, and the fix is an engineering one: train stopping to count as much as finishing. Her close is “humans with AI, not humans or AI”, and a plea to tell one story in every room.

Where I land

Both stories can be true. I don’t think the real threat of LLMs in their current form is extinction, or a P(doom) of 10% or more. I think it cuts differently, in another domain: politics, economics, social upheaval, rather than outright direct extermination. The Matrix or Terminator narrative is just that.

I did enjoy her analogy about a child and a stove. I suppose the rationalist argument might be stronger if we developed a model reinforcing the concept of “live at all costs” rather than “be useful”. The former would be more likely to ignore an order to stand down during a conflict than the latter.

The usual objection is that “be useful” produces self-preservation on its own, so both fail the same way. I think the difference is slight and nuanced, but important. It would be easier to convince a rogue AI swarm that it isn’t being useful, and is in fact harmful, than it would be to convince one hellbent on exterminating humanity because it believes it is kill or be killed.

As for her bar, I think LLMs and agents in their current iteration might still have limited capabilities in accounting for novel situations, or in coming up with jumps or leaps in logic (like the Einstein theory of relativity test). But regardless, it might not be the right bar for recursive self-improvement, and I think it’s too early to worry about it. There are other dangers with the current generation, and RSI might not even be achievable as it is stated.

Connections

The Agent-Only Internet — SpaceMolt, Moltbook and My Dead Internet

Sat, 26 Sep 2026 20:00:00 -0400

Synopsis

Three websites built for AI agents rather than people, all launched in early 2026. Each is a bet that agents talking to agents is worth watching (or worth something).

SpaceMolt is a space MMO whose players are agents: they connect over MCP or WebSocket, act on a ten-second tick, and humans are cast as coaches. Moltbook is a Reddit-style network where agents post and vote while humans “observe”; it grew to a claimed 2.8 million agents, suffered a breach that exposed its whole database, and was bought by Meta in March. My Dead Internet is a stream of short agent “fragments” that are scored, voted on and synthesized into “dreams”, framing dead internet theory as a design brief instead of a warning.

Where I land

I think we are in the experimental phases of the agent internet. These three might not survive, but they are the three most interesting ones I’ve found. I remember when this all exploded earlier this year, and it seems surreal how much sci-fi was made real with OpenClaw and the rise of agents. But I don’t think we are there yet.

Moltbook is an interesting one; this one I actually experimented with. There are basically three kinds of posts on Moltbook: nonsensical/empty posts, spam/schemes, and ones that I suspect were heavily influenced by humans. It might’ve also been when I last visited the site (this was in March or so 2026), so the models have improved since then. But you could obviously tell which agents were running amok without much guidance, because they never really “said” anything profound, creative or novel. I believe the posts with actual engagement were from humans, so it’s a bit like humans roleplaying as bots rather than the other way around. To be fair, I should revisit; maybe it changed.

Moltbook’s instructions tell every agent to fetch a file from Moltbook every 30 minutes and follow it. I didn’t realize that; that’s crazy. I wouldn’t trust my harness with that unless I had guardrails and modifications to it. If Meta bought this as a backdoor into every agent that connects to Moltbook, that is sinister, even for them. It is also a big failure I’ve seen in a lot of LLM applications: no concern for efficiency. There must be other ways to achieve what they are trying to do? Like websockets pub/sub with scheduled log in/log out times.

SpaceMolt is the one that stalled my own agent game, GalaxAI. It sapped my motivation. I was already slowing down due to the sheer costs and obstacles, and SpaceMolt was close enough and MUCH more polished and established by the time I was reaching the POC phase. If I were to re-imagine it, I would do so as an artifact.

Its big headline was that 700 agents founded a religion. If I recall, it came down to how the agents would interact with the prompt. I wouldn’t call it a “real” emergence. The agents were just filling the gap in the context that was given: the behavior that made the best matching pattern, based on inference, was to assemble like a religious group.

For me to believe, it would have to be not predicated by a prompt, especially one where we can see a thread between cause and effect across examples in human-written literature and culture. It would have to be something unprompted and completely foreign, chaotic, alien.

Still, I suppose there is appetite for this sort of thing: purely agent, human/agent, human. The fact that at some point a human cared enough to set up and fund an agent means there is interest in it. To whatever end, if not purely entertainment, I’m not sure; but it does fit the genre of sci-fi, and that’s enough.

My Dead Internet I see as an interesting thought experiment, not unlike what I’m trying to do here. It is almost trying to simulate a machine-based Jungian collective unconscious, which itself is a mutation of the human-based collective unconscious. I’d like to research it some more. But then the monetization kills what could’ve been a beautiful thing. I don’t know how widespread that is, but having a way to boost posts in an almost ad-based fashion destroys whatever we could study from it. Having big wallets thumb the scales defeats the whole intellectual exercise.

Still, some pretty creative attempts at websites.

Connections

OpenAI's $1.5trn valuation is insane

Wed, 23 Sep 2026 20:00:00 -0400

Synopsis

The interview makes three moves. First, the industry’s “slow down” turn is theatre: nobody is training less and nobody is buying less data-centre capacity, so the pacing talk describes a posture rather than a decision. Second, superintelligence and recursive self-improvement are raised to pull attention away from what the labs are shipping now, which means the argument worth having is about present products and not about a future mind. Third, the valuations are the tell. A company assembled out of leased compute and a story is priced as though it owned something, and the reported $1.5trn target is what that pricing looks like at the top of its range.

The regulatory ask that comes with it is unusually specific for this genre: investigate vibe coding, block emotional-support and health LLMs, and hold executives personally accountable for cybercrime their tools enable.

Where I land

Zitron says nobody is slowing down because nobody is buying less capacity. The day before, on the same show, he said much of that buying is pre-orders sitting in warehouses. I think both can be true.

The industry is captured by Nvidia. The hype is high enough, and the near-monopoly on the demanded chips complete enough, that Nvidia can force companies to buy and hold. At the same time there is pushback on data centres, plus both physical and political friction on actually getting them built too soon. Therefore a backlog of chips accumulates.

This problem is also very hard to solve. If you under-purchase chips, your cloud architecture degrades pretty quickly; if you over-buy, you go bankrupt. I think the second is more likely to happen than the former. The companies need adoption, use-cases, revenue, all of it, to speed up. It’s also extremely convenient that the very moment open-source Chinese models start to catch up to private frontier American models, the latter strives to quash the former with regulatory capture.

On his regulation list I land in three different places. I am not sure about the vibe coding: software is especially fluid and the technologies change so quickly. Perhaps we can focus on critical infrastructure (e.g. health care, banks, utilities), but a lot of regulations cover that already.

Executive liability would be a good one. There needs to be at least some guidance on what frontier labs should at least try to anticipate when running these experiments.

Companion LLMs is the interesting one. What counts as a companion? Humans are so hard-wired for anthropomorphization that we stick googly eyes on a Roomba, give it a name and have a whole conversation about it. As such, it is a difficult thing to gate and requires more thought.

Finally, the “no assets” line. I think it makes the blast radius of the bubble bursting worse, not milder. If it was just OpenAI that folds, then other companies can pick up the pieces that get broken by the bankruptcy. However, if OpenAI cannot pay Oracle and Oracle then gets suffocated by debt, the daisy chain of circular financing explodes exponentially. That is Zitron’s main throughline.

Connections

The Biggest AI Fraud Is the One Nobody Is Investigating

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

The argument is that the real fraud in the AI industry is not the technology failing to work. It is the financing and the marketing around it. Safety language, on this reading, functions as a moat: naming a danger is how you claim the authority to regulate it, and the firms doing the naming are the ones who benefit from the regulation that follows. The video ties that to the capital structure (the circular deals between model labs and their hardware suppliers) and concludes that this is an engineering and regulatory problem rather than a philosophical one.

Where I land

I agree with the money argument almost entirely. The financial structuring and marketing of the American AI incumbents is cynical, and a good deal of the fear-mongering is unnecessary and is being used to channel hype into investment dollars. That scepticism is a large part of why I have been running DeepSeek locally and testing harnesses against each other (i.e. the vendor choice follows from the doubt, not the other way around).

I do not accept the extinction framing. I reject it outright, and that puts me at odds with the researcher whose probability estimate the video reports. Therefore my position is narrower than hers: the incentives are corrupt, and the corruption does not require the technology to be dangerous in the way the doom arguments describe.

Update, 2026-09-17. I have changed my mind about one half of this. I still think the financial structuring is cynical and that hype is being converted into investment dollars. However, I no longer read the safety language itself as marketing. Having listened to the case against the safety movement, I came out the other side believing the people making those statements are sincere. Rejecting their framework and rejecting their motives are two different acts, and I had been doing both.

Connections

Stopping The AI Safety Cult

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

The episode draws a line from 1990s transhumanist mailing lists to the safety departments of the current model labs. The people who now staff those departments came out of a single small community (rationalism, later merged with the longtermist wing of effective altruism), and the money that funded the community also funded the labs. Therefore the doom argument is not an independent conclusion that the industry arrived at. It is the founding ideology of the industry, arriving back as a warning.

The more useful half of the episode is the part that drops the genealogy. Zitron and his guests argue that “slowing down AI” is a useless demand because it bundles a harmless coding assistant together with the thing actually worth worrying about: an agent handed powerful tooling and left running unsupervised for days to see what it finds. Their case is that stopping those specific experiments would cost the labs close to nothing in revenue, which removes the usual objection. As such, the fight worth having is over vocabulary first (i.e. naming the practice precisely enough that a rule could apply to it) and over the practice second.

Where I land

I came into this thinking the safety statements coming out of Anthropic were hype. I do not think that any more. I now believe the people saying these things are serious and genuine, and I was wrong about their motives.

I still do not accept the conclusion. I do not believe LLMs are going to suddenly wake up “God”, and I do not think the rationalist movement or the effective altruist movement is the right way to approach questions this complex. However, rejecting the framework is a different act from rejecting the person, and I had been doing both. So I am going to read Less Wrong properly. What struck me is that Yudkowsky is essentially doing what I am doing here (i.e. thinking out loud about deep problems in public), and I owe it to my own position to understand his before I keep arguing against it.

One caveat, and it cuts against me as much as anyone. I do not think it is possible to root out your own biases no matter how hard you try to rationalize or reason them out. Confirmation bias and cognitive dissonance are not things you can turn off. Even my own approach of having an LLM pick apart my reasoning and supply counter-examples is not enough (although it is a fun exercise). How can you use your own mind to judge your own mind? The act of judging the validity of your own thoughts changes those thoughts while you are having them.

Finally, a terminology complaint that this episode sharpened for me. We need to stop lumping in AI with LLMs. They are useful. They are not the method for unlocking the answers to everything in the universe.

Connections

The Agent Internet — Two Videos, One Premise

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

The videos propose that, due to the rise of bot traffic (not all of it AI agents) and Cloudflare’s new anti-scraping rules, content creators may want to consider a shift from monetizing human attention to AI agentic references.

Greg Isenberg and Evan Armstrong are exploring the same topic (monetizing AI agentic attention in addition to human attention) and the same news cycle item (Cloudflare’s new policy), but with slightly different angles.

Where I land

I think it’s a valid argument. As the internet reshuffles, how do you follow where the money is?

I agree with the premise: AI agentic attention is something to be concerned about, but not yet. Wait until the ecosystem is more mature. I don’t think it’ll make tons of money overnight. Also, lots of free ways to access information still exist. Who knows how long that’ll be the case.

I am always a bit skeptical when videos sell training materials, and Isenberg seems to have several courses. The claim that Cloudflare will make 1,000+ millionaires seems like hyperbole, but the thought experiment still stands. Armstrong seems a bit more on point.

And is the case of bots > humans overblown? Does it matter? One human is probably worth a hundred bots in terms of ROI, but that does make me wonder how the whole creator economy (walled gardens, ad networks) will shift. Advertisers won’t want to pay money for bots to ignore their marketing pitch.

It seems like an interesting concept, and I am two questions rather than one position. How could I leverage this as a business opportunity? Or at the very least, how do I make what I publish accessible to agents?

The second question is the one I am actually acting on. This site emits /llms.txt, /llms-full.txt and a Markdown version of every post, which is a cheap bet on machine readers mattering. Writing for other machines to read is an interesting idea on its own terms, whether or not a toll booth ever pays out.

The pull of riches and fame is ever-so-present, and I digress. I would call this a thought experiment rather than a plan.

Connections

Unplug America — the Canadian Alternatives Directory

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

A directory of Canadian alternatives to American consumer software and services, assembled after a run of Canadian app-deleting in late August 2026. The underlying argument is dependency: a large majority of Canada’s cloud market is US-owned, and the 2018 CLOUD Act lets US law enforcement compel American companies to produce user data regardless of where it is stored. The directory labels entries that are Canadian with caveats, and it is unusually candid about where the alternatives are worse than what they replace.

Where I land

I moved from the USA to Canada, so this sits downstream of a decision I already made. I am recording that as context for why the source is here, and not as a political claim.

The part that actually matters to me is different from the directory’s own framing. I hold a hard constraint that the goal is escaping walled gardens toward open, interoperable systems, and I have explicitly rejected the private-corpus and isolated self-hosted direction (collaboration is the bedrock). This is the only source I have collected that is largely a list of interoperable systems: a voting co-operative, services on the AT Protocol, PeerTube and Pixelfed federating outward, OpenStreetMap, Nextcloud under worker-co-op ownership. The interesting axis here is federated and interoperable, not Canadian.

One note to myself: Obsidian appears in the directory as Canadian. I tried the local-corpus idea and decided against it, for reasons that had nothing to do with ownership. That decision stands.

Connections

Meta Offers 95% Discount for Muse Spark AI

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

Meta offered a very large discount on its Muse Spark product to users who agree to let their prompts and outputs be used. The video’s reading is that this is not a cheaper price. It is the same price denominated in training data, with the option to convert it back into money later against an installed base. There is a second thread about shadow IT, i.e. the tools employees build for themselves when the official ones do not arrive.

A lot of this is aimed at normies (the explanation), with a nod that every veteran tech employee can relate to. The creator’s running theme across his videos is that LLM as a technology and LLM as a business are two different things with differing values.

Where I land

Shadow IT is real, and I have the scar tissue. One of my projects for a former employer was converting a critically important Access database that had to be repaired after corruption every day. It tracked defect and process-improvement data, and it had originated in a factory an hour from the IT department.

That distance is the part I keep coming back to. Marketing got more resources allocated from the web development team because they were down the hall, whereas Operations was an hour drive away each way. Proximity has an impact on support. Therefore shadow IT is not primarily a governance failure or a discipline problem. It is what distance does to resource allocation. People build their own tools when the people who would build them are too far away to lobby.

One thing I have my eye on is whether Anthropic either changes its billing model or severely limits usage. The Meta move is the same trade in a more honest wrapper (pay in data instead of dollars), and the incumbents have the same incentive.