Neocloud 本质就是在算力暂时稀缺的时候，靠着后面干爹的支持，高杠杆的做多算力的投机模式。用了个 neocloud 的名字，想塑造高大上的形象，但背后实质就是个高杠杆高负债的二道贩子。算力不再稀缺时他们第一个出问题。如果干爹要帮着他们强撑下去，干爹的资本负债表也会遭受重大负面影响。

如果一个公司欠了 110亿美元的债务。平均利率超过 10%，那这就是名符其实的次级贷款了。 如果债务还在不断增加，就是非常危险的信号了。

BREAKING: Tesla's Roadster flips off a ramp and hovers back to the ground. "Flying cars are real."

With CRWV at 74 in pre-market, I’m now -37% since my all-in. I woke up this morning to my wife packing a suitcase. She’s moving in with her boyfriend, who is 100% allocated to SPY. I begged her to be patient and explained that CRWW will bounce back due to Jevon’s Paradox, but that didn’t help. “Who’s Jevon? Is he good looking and rich?” She replied. My life is over. Citadel ruined it.

With CRWV at 74 in pre-market, I’m now -37% since my all-in. I woke up this morning to my wife packing a suitcase. She’s moving in with her boyfriend, who is 100% allocated to SPY. I begged her to be patient and explained that CRWW will bounce back due to Jevon’s Paradox, but that didn’t help. “Who’s Jevon? Is he good looking and rich?” She replied. My life is over. Citadel ruined it.

Surprise, surprise - the race to fund AI data centers first is now backfiring on the big banks “Six months ago, banks were racing each other to underwrite data center loans tied to Oracle, Meta and the rest of the AI buildout. Now some of the same banks are trying to get that debt off their books. JPMorgan, Morgan Stanley, SMBC and MUFG have been exploring significant risk transfers, according to a report from the Financial Times. These are deals that let a bank keep a loan on paper while shifting its riskiest slice to outside investors. The banks are doing this because they are bumping up against internal limits on how much AI-linked exposure any one lender can hold….” - @Startup_Fortune

Two tweets, juxtaposed. No comments offered.

The Nobel Prize in Literature 2026 vs 1907:

Every few weeks, a frontier lab announces that AI has touched the summit of human thought. Here is how the machine actually works. A lab can throw a system at a hard problem millions of times, overnight, cheaply. When one attempt finally works, that attempt becomes the press release. The failures live in a log you will never see. The lab runs the search, owns the logs, picks the survivor, sets the test conditions, times the release, and films the explainer video. In any other industry that's called an ad. Pharma ran this playbook for decades. Publish the winning trials, bury the losers. Regulators finally forced preregistered trials with independent monitors. We don't let drug companies grade their own homework on billion-dollar medicines. We should stop letting AI labs grade theirs on hundred-billion-dollar machines. Consider the most celebrated case making the rounds, the AI-assisted counterexample to the decades-old Jacobian conjecture, which the hype machine instantly promoted into a referendum on machine genius. A counterexample is a single carefully constructed object. Finding one is a needle-in-a-haystack job, exactly what machine search plus a human guide is built for, and checking one is mechanical, like scanning a barcode. So take the result at full value, and then ask the only follow-up that matters: can it do it again? If this is machine reasoning rather than machine luck, the same system should be able to solve the same class of problems on demand. It should produce similar constructions for the neighboring conjectures in that family, work the variants, and hit targets that someone outside the lab chose in advance. A system that finds one object after a million hidden attempts has proven the search works. A method proves itself by working twice, on problems picked by strangers. There's a simple test for real capability: adoption. When computers conquered chess, the proof never appeared in a press release. Within five years, every serious player on earth had a stronger-than-champion engine on a laptop. Textbooks rewritten, profession permanently transformed. Same story with Go. Real capability escapes the lab and colonizes the field. Walk into a math department today and you'll find the same whiteboards and the same stuck grad students. And notice the simplest tell of all: you cannot buy it. No product accepts your unsolved problem. The capability gets announced, then never ships. Available means adopted. Announced means advertised. Then follow the money. These are private companies with the largest raises in business history, valuations in the hundreds of billions, revenue many times smaller. The gap between those numbers is filled with a story. Every capability announcement is a financial event wearing a lab coat. Watch the calendar: announcements cluster around funding windows, and funding windows open toward the IPO valuations waiting behind them. Here's the absurdity at the center. A machine that could genuinely solve hard problems would be the most valuable commercial instrument ever built. Batteries, drugs, chips, pricing. Its owner would compound the largest silent fortune in history. Instead the capability appears in a blog post and evaporates. What actually gets sold, at scale, today: words by the token, subscriptions by the month. The revenue mix is the confession. Now the tells the insiders can't fake. When employees know a goldmine is real, they quit and quietly mine it. Top AI people do leave, constantly, and they go to chatbot startups and agent tools. Nobody leaves to launch the fund that never loses. The departure pattern prices the insiders' honest opinion of the story: model and hype, in that order. And the locks. Your bank, your messages, your passwords all run on standard mathematical locks. If their machines had picked those locks, the labs would switch their own locks first. Cheap for them, invisible to you. They haven't. Same TLS as your bank, public bug bounties on the very same locks. There's precedent: in the 1970s the NSA secretly knew an advanced method of attacking codes, and it left fingerprints, quietly strengthening the official standard against that exact attack. Holders of real secrets leak through what they build. The labs leak nothing. Their infrastructure is a daily confession that standard math holds. For honesty, there's a control case. AlphaFold entered blind competitions graded by independent judges, won decisively, became standard equipment in every biology lab on earth, and collected a Nobel. Blind test, adoption, a product you can use. The math announcements skip every step of it. And the honest numbers tell you the true scale: Google's own math-search showcase saved one multiplication step in a standard calculation, the first improvement in over fifty years, plus a fraction of a percent of compute reclaimed across its data centers. Real. Useful. Small. The distance between that and the destiny in the press releases is the marketing budget. So the next time an announcement is pushed on social platforms, ask what the company is raising, and when. Ask where the attempt logs are. Ask whether the system can do it twice, on a problem a stranger chose. Ask whether you can use the capability this quarter, on your own problem. Ask whether working mathematicians adopted it, and whether insiders act like the story is true, and whether the test was blind and independently run. A lab that believes its own headlines welcomes every one of those questions. A lab that dreads them has told you everything. The pattern, every time: a search finds a needle, the needle becomes a narrative, the narrative becomes a valuation, and the capability never ships. That's the tell. Math deserves better than being collateral for a financing round. So do you.