Good day . Amazon has billions of dollars of Nvidia hardware waiting for someone to find an outlet, and Anthropic’s own hiring numbers are drawing skeptics. Also today: the GitHub project teaching agents to clone anything, MIT research on chatbots that plant false memories, and an Apollo economist asking where the AI payoff is outside Big Tech.
Amazon Bought the GPUs. Outlets Pending. Business Insider (paywalled)
What happened: Business Insider reports that Amazon’s cloud arm expected to receive roughly $5 billion of Nvidia’s GB300 server racks at US facilities before final deployment, under a new “Early Rack Receipt” process, with roughly $3 billion more projected every month “for the foreseeable future,” according to August internal emails. Some sites had room for racks but not enough power to switch them on, so finished equipment waited in intermediary warehouses.
Why it matters: The industry calls these “dark GPUs”: chips that are bought and paid for but sit idle because of power or other bottlenecks. Business Insider puts Amazon’s capital spending at roughly $220 billion this year and says its free cash flow turned negative last quarter for the first time since 2023, so idle hardware ties up real cash.
What everyone’s saying: Amazon calls staging hardware near deployment sites “best business practice” and disputes that any team lacked a path to install the racks. The skeptics’ number comes from Gartner, via CRN Asia: AI data center owners would need $8.5 trillion in cumulative revenue through 2030 to earn a 12% return, against a forecast of no more than $3.6 trillion.
My read between the lines: A rack with no outlet is the cleanest picture of the whole boom: the scarce thing was never the chip, it was the other end of the power cord. Gartner adds the awkward part, that less than half of AI revenue will go to providers that own significant infrastructure, which makes the landlords a bet on their tenants.
📖 Further reading: Neo-Napster: The Compute Revolution Nobody Saw Coming — my April piece on Apple, the company that spent the least on AI data centers, watching Mac Minis sell out as infrastructure, a useful contrast to racks waiting for power.
Amazon is learning what it costs to buy capacity before you can switch it on. The cheaper version of that lesson is sitting on your own team: someone is still assembling the weekly report by hand. Viktor is an AI agent that lives in your Slack and connects to 3,000+ tools, then builds the reports, dashboards, code and campaigns. Not a chatbot. A coworker. New readers get $50 off their first month. Hire Viktor →
Anthropic’s Junior Tech Hiring Fell to 9% Anthropic
What happened: In its report “How employment is changing at Anthropic,” the company says the junior share of its technical hires fell from 27% to 9% since early 2025, and juniors’ share of its technical departments slipped from 21% to 14%. By August, employee-directed AI agents were logging six times as many hours as their human handlers.
Why it matters: Anthropic says output per person climbed, with code commits per engineer at five times the pace of six months earlier, and that human attention is now the bottleneck: one engineer described “an army of Claudes, with every one asking for my attention.” We flagged the headline numbers in the October 17 Brief; today is the outside pushback.
What everyone’s saying: The Guardian, via Yahoo, quoted skeptics. Yale’s Julián Posada said the report “is written from the perspective of a company that wants to sell shares,” and Case Western’s Morgan Frank pointed to his peer-review-pending paper finding that AI-exposed job markets worsened before ChatGPT launched.
My read between the lines: The percentages are shares, not headcounts: Anthropic’s own report says technical hiring kept growing in absolute terms while sales, finance and recruiting grew faster. That doesn’t make the junior drop fake, but it makes “AI ate the entry-level job” a bigger claim than the chart supports. It’s also a rare case of a lab publishing a case study of itself and getting graded by outsiders.
📖 Further reading: The Tools That Just Replaced 40% of Block’s Workforce Are Free in Your Browser — my March piece on Block cutting 40% of its staff and why I argued the layoff panic is better news than it sounds, relevant to anyone asking what happens to entry-level work.
The Brief is free and always will be. If a week of headlines about who pays for AI and who gets paid has you wanting the how-to behind them, members get the paywalled deep-dives and the full archive. Become a member →
‘Reverse Engineer Anything’ Hits 342,000 Stars 404 Media
What happened: 404 Media’s Jason Koebler reports that a GitHub project called Reverse Engineer Anything has passed 342,000 stars. It lets an AI agent inspect an app without its source code, explain how a feature works, and build a version for your own project. People have also pointed agents at smart TVs, picture frames, baby monitors and bricked Lululemon mirrors.
Why it matters: Cheap cloning changes who can copy whom. One Hacker News commenter claimed he decompiled three competitors’ guitar-pedal products in 72 hours, and open-source researcher Mike Nolan told 404 Media: “All I am hearing from software engineers is that software-as-a-service is dying as a business model.” We covered the TV side in the October 20 Brief.
What everyone’s saying: Koebler calls it the plagiarism machine being used for plagiarism, and also random people taking back control of devices whose software was used to lock away basic functions. Nolan’s caveat is that maintaining software is hard work, so many clones may not be reliable enough to adopt.
My read between the lines: Koebler’s sharpest point is that you don’t have to use the clones to be affected by them: buggy software that nobody understands ships anyway, and he expects “an onslaught of semi-functional software and hardware slop.” Same tool, two stories. A locked-down TV loses its ads, and a pedal maker’s algorithm gets copied. Which one you cheer for depends on which side of the box you’re on.
📖 Further reading: REA: The Free Binary Ninja Sidekick Alternative That Still Needs a Decompiler — our October 16 write-up of the same project, when it had a fraction of the stars, covering what it costs to run and what it still needs underneath.
Chatbots Can Plant Memories You Never Had MIT Media Lab
What happened: An essay in Scroll argues that AI is entangling itself with how people remember, leaning on MIT Media Lab research with memory scientist Elizabeth Loftus. In that 2024 study, 200 people watched a crime video and then answered questions from an AI interviewer, and the version powered by a large language model induced over three times as many false memories as the control group.
Why it matters: The effect wasn’t marginal: 36.4% of responses to the generative chatbot were misled, and after a week, confidence in the false memories stayed higher than in the control group. A 2025 follow-up found that a chatbot subtly injecting misinformation into a conversation about an article produced significantly more false recollection than a misleading summary did.
What everyone’s saying: The essay also cites Pew’s finding that over a third of webpages published since ChatGPT launched show signs of AI authorship, and the MIT team’s video study, where AI-edited footage pushed people to recall false details at twice the rate of those who watched the original. Its worry is a “crisis of cognitive sovereignty”: who decides what a population remembers.
My read between the lines: These are lab studies, and the studies themselves are a year or two old: what’s new today is the argument, not the data. Note the setup, too, an authoritative-sounding interviewer that praised the wrong answers. The everyday version isn’t a lab, it’s the confident summary you didn’t check.
Outside Big Tech, AI’s Payoff Is Missing Apollo
What happened: Apollo chief economist Torsten Slok wrote on October 24 that AI has so far produced no financial payoff for US companies outside tech. Revenue per worker and profit margins are rising for the Magnificent 7 and flat for the other 493 companies in the S&P 500, and he says mainstream firms are starting to treat AI token spending as a cost that squeezes margins.
Why it matters: A real-world version of that cost line: The Next Web, following 404 Media, reports that Accenture now pays for only $29 of Claude use per employee per month as of October 1, after some staff had spent up to $1,000 a month. Anything above the cap goes to a project budget code, and Microsoft, Atlassian and Uber have also introduced AI spending limits.
What everyone’s saying: Gary Marcus pulled Slok’s numbers into an October 24 post asking whether generative AI does more harm than good. His answer is that the case for net harm is now serious, citing deepfakes, scams, disinformation and the hacks some are calling “Black October,” which we covered as AI-found PHP bugs in the October 22 Brief, while granting that programmers get real value.
My read between the lines: Slok’s point is that the story gets decided where the customers are, not the sellers. Accenture is the cleanest test: a firm that sells AI transformation to others just capped its own staff’s Claude budget, while its own survey says 82% of executives are increasing AI investment. Both can be true: spend more in the strategy deck, less on the invoice.
📖 Further reading: I found 350,000 tokens hiding in plain sight — my April piece on auditing my own scheduled tasks for token waste, the personal-scale version of a company capping its Claude bill.
That’s your AI Brief for Sunday.
—Nicholas from Artificially Intimidating
