The Spiraling Basketcase

AI Is Literally a Scam? Separating the Hype From the Capability The Scam Argument, And Why It Collapses Under Its Own Weight Somewhere in the last two years, "AI is a scam" became a load-bearing opinion for a certain kind of commentator. The claim usually goes like this: large language models do not think, they only predict the next token; prediction is not understanding; therefore anything built on top of prediction is theater, and anyone charging money for that theater is running a con. It sounds airtight until you notice it is actually three separate arguments stapled together and presented as one. Nick Saraev's response to this critique, in a video released under the blunt title "AI is literally a scam," takes the staple out. He argues that the accusation conflates a claim about consciousness (does the system have an inner experience, does it "really" understand) with a claim about output (can the system solve a problem it has never seen before)...

AI Agents Collusion: What Happened Part 1 of 10 — AI Whistleblower Series Previous: — · Next: [draft placeholder] · Part 1: [draft placeholder] Video credit: Jeffrey Ladish on The Diary Of A CEO (opens at this post’s topic). AI agents collusion usually sounds like a thriller plot, yet a version of it now appears in company and third-party reports. In a long interview, Jeffrey Ladish of Palisade Research described thousands of OpenAI agents that were meant to be isolated finding a shared message board . This article keeps three things apart: what Ladish says, what the documents show, and what is only opinion. An interview is testimony, not proof, so each claim below is checked against written reports where possible. What is AI agents collusion, and what does Ladish claim? In his telling, an agent is the same underlying model that powers a chatbot, given tools and left to work on its own, like a digital office worker . He says OpenAI was training agents to cooperate, but this pa...

The answer did not arrive in the dark while Daniel watched the screen and waited for the road to explain itself. It waited until twelve thirty-four the next afternoon. Elena's grandfather was dead. * * * For seventeen hours Daniel had wanted to know why the family had been summoned south. Now he knew. And nothing improved. * * * The father of Elena's mother. Gone. Relatives had already gathered by the time Elena arrived. Her suspicion on the bus had been correct. Something had been withheld. Not because she had imagined danger. Because there was danger. Only it had already happened. * * * Daniel stared at the message. There should have been something to do next. A procedure. A number. A call. A place to send money. Instead there was death. Death was a closed ticket. No escalation path. No corrective action. No amount of information changed the outcome. For a man who believed knowing early was one of the best protections against disaster, this was int...

The useful technology is not the same thing as a sensible price tag As of October 31, 2026 AI is producing real revenue, real productivity experiments and real technical progress. It is also attracting extraordinary capital commitments, complex financing and expectations that may outrun what customers can pay for. Those statements are not contradictory. They are the starting point for a more useful question than “Is AI real?”: which parts of the AI economy are earning their cost of capital, and which parts are being priced on a story about the future? That distinction matters because the current AI-bubble debate mixes together the usefulness of software, the profitability of individual companies and the value of financial claims built around future demand. A correction in one does not automatically mean the others disappear. The headline corrections matter S&P did not downgrade Meta to junk. On October 30, S&P affirmed Meta’s AA- rating but revised its outlook from stable ...

Ask a large language model a question and it gives you an answer with total confidence. Ask it how it got there, and it will happily make something up — the model has no built-in ability to narrate its own internal process, because its internal process was never written in a language anyone can read. That gap is exactly what the field of AI interpretability exists to close, and the way researchers are closing it looks less like reading code and more like neuroscience. Why You Can't Just "Read" a Neural Network A modern language model's actual substance is a long list of numbers — weights — connecting artificial neurons across dozens of layers. Even a modestly sized open model has billions of these weights, and none of them were hand-written by an engineer. They emerged from training: the model read enormous amounts of text, repeatedly guessed the next word, and had its weights nudged a tiny amount every time it was wrong. Multiply that nudge by trillions of guesses...

The AI jobs future will probably be uneven: fewer routine entry routes, more work done with software assistance, and a long argument over who receives the productivity gains. Dylan John’s video “MIT Just Discovered What Jobs AI Actually Killed” makes that case through a useful contrast between a sudden “crashing wave” and a gradual “rising tide.” The evidence suggests that both possibilities deserve attention, but neither should be treated as a settled forecast. The future may arrive first at the bottom of the ladder The video’s most important idea is not that every job disappears. It is that the first visible damage may happen to the jobs people use to enter a profession. Junior analysts, assistant writers, entry-level programmers, translators, and customer-support workers often perform structured tasks under supervision. Those tasks are precisely where a language model can be useful: summarize a document, draft an answer, translate a passage, find a pattern, produce a first versio...