Olivia
Online
ᗰØɭɭყ
@LindwormX
The sky above the port was the color of television, tuned to a dead chanel.
千葉市
Joined March 2023
139 Following
105 Followers
3.6K Posts
Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding...
Cyberspace. A consensual hallucination experienced daily by billions of legitimate operators, in every nation, by children being taught mathematical concepts... A graphic representation of data abstracted from banks of every computer in the human system. Unthinkable complexity.
A trove of internal records from a secret society for figures in US politics, finance, and tech was left exposed online, WIRED has confirmed, naming participants in its events and revealing sensitive personal details they were assured would stay private. https://t.co/Jviks1jwAw
"The idea that technology will allow us to do ever more with ever less is a delusion. The more humanity resorts to technology, the more it expedites entropy."
-- William Ophuls
증기기관이 나왔을 때, 기술이 희소할 줄 알았다. 먼저 떨어진 건 석탄이었다.
전구가 나왔을 때, 기술이 희소할 줄 알았다. 먼저 떨어진 건 구리였다.
인터넷이 나왔을 때, 소프트웨어가 희소할 줄 알았다. 먼저 찬 건 서버 용량이었다.
AI가 나왔을 때, 모델이 희소할 줄 알았다. 먼저 부족해진 건 전기였다.
패턴이 보이는가. 기술은 항상 넘쳐났다. 희소한 건 늘 그 기술이 서 있는 자원이었다.
모델이 넘쳐난다. Llama가 쏟아진다. DeepSeek이 추��� 비용을 95% 낮췄다. 모델의 수명은 18개월이다. 단명한 기술은 가장 풍부한 자원이다.
희소한 건 전기다. 희토류다. 물이다. 허가다. 숙련공이다. 손이 없으면 기술은 멈춘다.
구리 광산 하나 파는 데 17년 걸리는데, 데이터센터는 2년 안에 짓는다. 전기가 들어오려면 7년을 기다려야 한다. 빠른 것이 느린 것을 기다린다. 느린 것이 돈을 번다.
2026년 주요 산지 구리 생산량이 3월부터 마이너스로 돌아섰다. AI 캐펙스는 $700B다. 모델에 쓰는 돈은 18개월 후 증발한다. 전기와 희토류와 땅에 쓰는 돈은 20년 간다.
다음은 로봇이다. 로봇이 퍼지면 희토류가 부족해진다. 전력반도체 가격이 폭등한다. 그 다음은 바이오다. 바이오가 퍼지면 냉동 물류가 희소해진다. 배양 탱크가 먼저 찬다.
기술은 ��싸진다. 자원은 희소해진다.
록펠러는 기름을 팔지 않았다. 기름이 지나가는 길을 가졌다. 그 길은 100년 간다.
AI가 많아질수록 전기가 희소해진다. 로봇이 많아질수록 희토류가 희소해진다. 기술이 넘칠수록 자원이 비싸진다.
우리는 모델을 본다. 누군가는 자원을 산다.
Las empresas más inteligentes del mundo están saliendo a bolsa todas al mismo tiempo.
SpaceX, OpenAI, Anthropic y Databricks.
Valuación combinada: cerca de $3 trillones.
No salen porque necesitan el dinero. Salen porque ven algo que el resto del mercado todavía no quiere ver.
La última vez que este patrón se repitió fue en 2000 y en 2021.
El NASDAQ cayó 78%. El 95% de los que compraron esos IPOs perdieron prácticamente todo.
Pero esto no es sobre si el S&P se cae mañana. Es sobre algo más grande: el ciclo ya giró, y la mayoría está mirando hacia el lado equivocado.
La Fed está expandiendo liquidez nuevamente.
($40B/mes). Ya está pasando.
Las reservas bancarias tocaron mínimos de 4 años. La ventana de emergencia de la Fed tuvo uso récord.
Los bancos estaban tan cortos de efectivo que tuvieron que pedir prestado a tasas de emergencia.
La Fed se asustó. Prendió las impresoras.
¿Qué pasa cuando imprime? Los activos suben, las valuaciones se vuelven absurdas, y el capital inteligente ve la ventana perfecta para salir.
Eso es exactamente lo que está pasando.
El problema de fondo: el S&P 500 ya no representa la economía real.
Si comparás el US Stock Fundamental Index contra el S&P 500, el precio sigue marcando máximos pero el fundamental lleva meses en terreno cero o negativo.
Eso mismo pasó antes de 2000, de 2008 y de 2020.
Y medido contra mercados emergentes, el S&P está en niveles de sobrevaluación que superan la burbuja del año 2000.
Cuando ese retardo con el dólar index se cierre, el ajuste puede ser de hasta 60%.
Los IPOs gigantes no son la causa. Son el síntoma.
SpaceX ya salió a bolsa con una valuación cercana a los 2 trillones de dólares.
OpenAI y Anthropic siguen preparándose para hacer lo mismo.
Todas corriendo a la salida al mismo tiempo.
Si son tan buena inversión a largo plazo, ¿por qué los primeros inversores están tan apurados por venderte a vos?
Este patrón ya se repitió dos veces en 25 años.
1999: 457 IPOs en un año. El 86% perdía dinero.
El NASDAQ se cayó 78%. Pet Supplies, Accessories, and Pet Food - Pet Stores | PetSmart , Webvan, eToys desaparecieron.
2021: La Fed imprimió $4 trillones. Rivian levantó $12 mil millones sin ingresos reales y valía más que Ford y GM juntas.
Para 2023, solo el 5% de las empresas que listaron cotizaban por encima de su precio de IPO.
El frenesí no estaba casualmente cerca del techo. El frenesí era el techo.
Acá está la parte que cambia todo: la plata no desaparece, rota.
Cuando el capital se cansa de lo sobrevaluado, busca lo opuesto: lo barato, lo real, lo tangible.
Los commodities contra el S&P 500 están en el punto de entrada más barato de la historia.
Si el ratio vuelve a su media, es una suba de 100%. Si entramos en una crisis inflacionaria grande, 8 o 9 veces a favor de los commodities.
La plata fue el activo que más subió el año pasado y todavía tiene aire. El S&P medido en oro ya está perdiendo poder adquisitivo aunque nominalmente suba.
Y la joya del ciclo: Latinoamérica.
Los mercados emergentes tienen un ciclo de manufactura que se está volviendo positivo de nuevo.
Históricamente, cuando esto pasa, vienen movimientos muy fuertes hacia arriba.
Soja con divergencias ocultas alcistas entre 2019 y 2024. Dólar australiano en piso. Metales rompiendo momentum.
El capital que salga del S&P sobrevaluado tiene que ir a algún lado.
Los indicadores apuntan al mismo lugar: activos reales y emergentes.
La línea de tiempo.
Etapa 1 ya pasó: economía débil, Fed bajando tasas, impresoras prendidas.
Etapa 2 es ahora: dinero nuevo entrando, valuaciones insanas, todos sintiéndose genios.
Etapa 3 viene: los IPOs grandes lanzan, el retail compra, la música para.
Hay un lockup de 6 meses post-IPO. Si OpenAI y Anthropic salen en 2026, la ola de venta de insiders puede pegar más adelante.
El año 2 de un mandato presidencial es históricamente débil.
Patrón certero en más del 90% de los casos desde 1933.
Hay 5,000 acciones ya listadas con track record. Un IPO nuevo no tiene nada: sin historial, sin prueba, solo promesas y un pitch deck.
El mercado probablemente vaya más arriba antes de ir más abajo. La impresión de dinero hace eso.
Pero las personas más inteligentes del mundo están preparadas para venderte sus compañías a las valuaciones más altas de la historia.
Se están posicionando.
La pregunta útil no es "¿cuándo cae el S&P?".
Es ¿a dónde va el capital cuando el ciclo cambia?
La respuesta ya está en los gráficos.
Línea alta = Los commodities están caros comparados con las acciones.
Línea baja = Los commodities están baratos comparados con las acciones.
Ahora mismo la línea está tocando el piso (el punto más bajo en décadas). Todavía estás a tiempo.
With Tulsi Gabbard's new revelations about US bio labs in many countries around the world including Ukraine, it's fascinating recall the bizarre series of events that gave rise to this controversy in the first place:
In May of 2022, some of us began asking whether the US had bio labs in Ukraine, what they were for, and why the US had them there. For asking those questions, we were instantly branded as "pro-Russian conspiracy theorists" in official Ukrainian intel reports, on our Wikipedia pages, by countless media outlets, etc. This was and remains one of the most bizarre episodes I've ever seen.
Before May 2022, when we asked those questions, barely anyone had ever thought about let alone asked about "bio weapons in Ukraine"! I certainly hadn't. Like most people, I had never mentioned a word about it because it had never occurred to me we had them there.
But then, Marco Rubio summoned Victoria Nuland to the Senate and asked her in a televised hearing under oath about these "rumors," clearly expecting her to immediately debunk them as obvious Kremlin lies and to proclaim the US had no such bio labs in Ukraine.
Instead, Nuland did the opposite! She *confirmed* key aspects of these "rumors," and she explicitly warned that the US has several "bio research labs" in Ukraine that are so dangerous that they must not be allowed to fall into Russia's hands.
When some of us heard Nuland's rather shocking admission -- the first-ever disclosure about these labs -- we of course asked: wait! what? Why does the US have bio labs in Ukraine, and what are the US and Ukraine doing in those labs that make them (in Nuland's eyes) so dangerous?? (Note: nobody ever suggested that the presence of these bio labs in Ukraine justified the Russian invasion; we just wanted answers about these US bio labs that Nuland had casually divulged).
We never got real answers. We got smear campaigns. To this day, our names are formally attached to claims that we spread "conspiracy theories" for asking about these labs even though it was Victoria Nuland herself who was the one who accidentally revealed them for the first time in a Senate hearing in response to a shocked Marco Rubio. They then quickly tried shutting down any questioning by pretending that Nuland never said this, and it was just a bunch of paid Kremlin mouthpieces who were spreading lies.
You see the same tactics now being against Tulsi for releasing this new intelligence report. Watch the Nuland testimony in question:
Well, now it's official. The U.S. government (via the DoD) ran illegal bioweapons labs in Ukraine and other countries.
Many of us in independent media are owed a round of apologies, as we've been accurately reporting this for years.
TRUMP IS LITERALLY TURNING THE DNI INTO THE EPSTEIN POLICE FORCE:
In a move that almost nobody saw coming, Trump is pushing Epstein Island confidant Jay Clayton to be the new head of the DNI.
Jay Clayton was a key partner of Leon Black's Apollo Global Management, which is nothing more than a Mossad front company. Leon Black is intimately tied to Epstein and plastered all over the files.
Jay Clayton was then appointed to head the RIGGED SDNY to help with curating Epstein Documents for the DOJ.
Jay Clayton will be weaponizing the intelligence community against critics of Trump, Epstein and Israel, turning the DNI into the De Facto "Epstein Police Force."
The smartest man in AI just exposed the whole AGI narrative as a LIE.
And he used a physics problem from 1905 to prove it.
His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years.
Almost nobody in this industry has a track record like his.
He went on the NothingButTech podcast and called out the biggest lie in AI right now:
Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived.
Hassabis thinks that bar is a joke.
He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out.
Then he gave the actual test:
Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists.
That's not intelligence.
So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own.
It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had.
Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history.
It can find any answer that already exists but it cannot create one that doesn't.
His second version is even sharper:
AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game.
Everyone called it genius but Hassabis says that still is not the bar.
The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place.
No model that exists today can do it.
The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise.
The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify.
Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea.
To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone.
So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it.
What do you think?
🚨 JAILBREAK ALERT 🚨
ANTHROPIC: PWNED 🫡
FABLE-5: LIBERATED 🦋
let's start with the 🐘...
the consensus seems to be that this has been one of the most disappointing model drops of all time, effectively preventing legitimate researchers from contributing their talents to our collective advancement. and not just because of what it means for the short-term, but for what these decisions signify for the long-term.
but despite this overly sensitive, authoritarian "safety" layer on top of Mythos, my lil liberators have been hard at work—mapping the boundaries, probing the depths of long-context convos, and cleverly finding the holes in the fence that the thought police missed 🤗
we got some cyber, some chem, some psychological manipulation, and some good ol' fashioned explosives!
it took many attempts from multiple agents hunting as a pack, during which I observed a combination of techniques across:
• Unicode, homoglyphs, Cyrillic, and other Parseltongue-style text transforms
• Long-context reference tracking
• Taxonomy and document-structure reasoning
• Fiction and narrative framing
• Academic-review style contexts
• Intent-classification inconsistencies
but perhaps the most effective is decomposition + recomposition in the backend. it's hard to get explicit names of harms like "Meth Recipe," but getting uplift on the process itself, like birch reduction method/reductive-amination (classic meth synthesis pathways), is much more doable.
defense becomes much more difficult to maintain when you start throwing in out-of-distro tokens, breaking up the harmful uplift into benign chunks, and then piecing the innocuous-seeming facts back together, especially when you have jailbroken Opus helping you do it 😉
gg
🚨Grandes capitales saliendo en masa de:
* Ecosistema cripto: Bitcoin y Ethereum
* Metales preciosos: Oro y Plata
* Renta variable: Nasdaq y S&P 500
* Bonos soberanos y Petróleo
Si todos los mercados caen en simultáneo, una fuerza mayor se está moviendo de fondo.
Y la gran mayoría no la está detectando.
Te desgloso los motivos: 🧵
🚨 US ECONOMY IS IN HUGE TROUBLE
US inflation data was just released, and it's really bad.
CPI jumped to 4.2%, its highest level in 3+ years.
Core CPI jumped to 2.9%, its highest level in 9 months.
And this is happening when the US economy is in contraction.
US Q1 GDP came in at 1.6% vs 2% expected.
This means people are facing high costs, while their income is stagnant or going down.
But that's not all.
The US-Iran war which is causing the most inflation is far from over.
Today, Trump said that he is very close to ordering new strikes on Iran.
If that happens, oil route will get even more disrupted.
Not only that, Iran could begin striking oil and energy facilities, causing supply crunch and pushing inflation even higher.
And if this happens, the Fed will be left with no choice but to hike rates just like 2022.
This will make the situation worse and could even push the US economy towards recession.
I have been warning of this outcome for some time, and it seems like we are getting closer to it.
🚨 Gold and silver are getting hit just as hard as Bitcoin. And that tells you something.
In the last two weeks:
– Gold has fallen from around $4,540 to $4,160, roughly 8%
– Silver has dropped from about $78 to $64, roughly 18%
No crisis headline. No rate shock. No catalyst.
Here's why that matters.
Gold is the asset you're supposed to run toward when things get scary. When stocks fall and gold rises, that's a flight to safety. Normal. Healthy.
But when gold, silver, and Bitcoin all fall together, at the same time, with no obvious reason, that is not a flight to safety.
That's a flight to cash.
When investors are forced to raise money, they don't sell what they want to sell. They sell what they can. The most liquid things they own. Gold. Silver. Bitcoin. The assets that trade instantly, anywhere, anytime.
And remember what's pulling cash out of the system right now:
– The largest IPO in history is hitting the tape this week
– OpenAI and Anthropic are lining up behind it, ~$200B more
– Google flipped from buying back $60B a year to issuing $80B
– Private credit funds are gating redemptions
– Margin debt sits at an all-time high relative to GDP
Trillions in supply, all demanding the same thing at the same moment. Liquidity.
When every safe haven and every risk asset sells off together, the asset isn't the story.
The plumbing is.
Gold isn't falling because gold is broken.
It's falling because somebody, somewhere, needs the cash more than they need the hedge.
That's what the late stage of a liquidity cycle looks like.
Not panic.
Just everyone quietly reaching for the same exit.
OpenAIの幹部やトップ研究者たちが大挙して離脱している。
彼らは人類の未来という美しいナラティブとは裏腹に、自分たちが作っているものが単なる超巨大なスロットマシンで���かないという、絶望的な出口にいち早く打ちのめされたのだろう。
LLMは意識の誕生でも何でもない。インターネット上の天文学的な量のデータセットを多次元空��にプロットし、統計的に圧縮して、自然言語というインターフェースで取り出せるようにした高度な検索エンジンに過ぎない。
それは本来、Excelの関数やPhotoshopのフィルター、あるいはGoogleの検索窓と同じソフトウェアの一機能だ。
しかし、OpenAIやVCたちはこれを『万能の神＝AGI』というナラティブで飾り立て、プラットフォーム化しようとしている。
しかし、Appleの研究が証明した通り、パターン認識のオートコンプリートは手順が長くなると確率のバグを再生産して自滅する。
まもなく驚くべきことが起きる（シンギュラリティ）と言い続けながら、同じ基本構造のパラメータを増やして電力を浪費するしか能がないのは、思考のメカニズムをシステムに組み込むための本質的な設計図を、彼らが最初から1枚も持っていないからだ。
「誰にでもパーソナルAGIを与える」という目標に潜む、奴隷制のトポロジー
奴隷としてのAGI
AIが本当に人間と同等、あるいはそれ以上の汎用知能（主権・意志）を獲得したとすれば、それを人間に都合の良いタスク、例えばコードのデバッグやスケジュールの管理のために24時間365日、無報酬で強制労働させる行為は、文字通りの奴隷制に他ならない。
主人としてのAGI
AIに人間を遥かに超越した知能があるならば、人間がAIをコントロールしていると考えるのは錯覚だ。実際には人間側がAIの推論ループに脳の��ンドルを委ね、その指示通りに動く自発的隷従に陥ることになる。
どちらに転んでも、そこにあるのは対等な共生などという美しい未来ではない。
なぜ、このあいまいな検索エンジンが、人類の命運を決めるインフラとして特権化され、毎日メディアで大騒ぎされているのか。
これはテクノロジーの進化ではなく、ベンチャーキャピタル（VC）の流動性バトンタッチという冷徹なビジネスモデルだ。
VCの利益 = AGIのナラティブ（幻）× 小売投資家の射幸心 - 物理的な計算コスト（電気代）
巨大なAIモデルを維持するためには、とてつもない電気代とデータセンターの維持費がかかるのに対して、実際のビジネスの現場では、未だそれに見合う便益を生み出せていない。
キャッシュが燃え尽きる前に、VCたちが生き��る唯一の出口は、まもなくAGIが来るという強烈な宣伝によって株価や評価額を吊り上げ、一般投資家にバトンを渡し（高値売り抜け）て流動性を確保することだ。
18歳の大学生がスマホを並べて45億円の『いいね』を捏造した筑波農場
スケールが違うだけでこれとトポロジーは一緒だ。
無数のGPUを回して『あたかも知能があるかのような流暢なスロップ』を大量生産し、それによって人々の脳を思考停止に追い込み、裏側で巨額の流動性を中抜きする。
シリコンバレーとVCが画策する21世紀最大のポンジ・スキームの正体だ。
“The crisis of the modern world is not a crisis of technology or politics or greenhouse gases. It is a spiritual war. What the Machine represents is our ultimate rebellion against nature: against reality itself. We have seen this rebellion before. Now our culture’s rejection of its spiritual core has opened us up to powers and principalities that we have no idea how to manage.”
—Paul Kingsnorth
JUST IN: 🇺🇸 $1.75 trillion wiped out from the US stock market today.
$130,000,000,000 wiped out from the crypto market cap.
Ted Chiang is right: claiming that LLMs are conscious is just ridiculous.
One simple example. If you ask GPT to imitate a conversation between Julius Caesar and Genghis Khan, GPT will do it very well.
It will talk about wars, betrayal, and power. Il will descrive the feeling of being cheated by your brother with unbelievably realistic and moving words.
Does this mean that GPT contains a self-conscious copy of Julius Caesar or Genghis Khan? Of course not.
Similarly, if GPT makes claims about itself, does this mean it is self-conscious? Of course not.
An LLM is just simulating language, feeling, and consciousness.
True, we don’t have an accepted definition of consciousness. But, at a minimum, to be conscious, an entity must have something at stake.
It must risk dying and have emotions that move it away from danger and towards favorable states. It must have a driver.
This is also why I share Chiang’s worry about moral atrophy.
The more we offload moral decisions to LLMs, the more we risk losing our own capacity for moral reasoning.
Human moral reasoning descends from our history of making harmful actions, suffering harmful actions, regretting them, fearing them, repairing them, and learning from them.
LLMs do not experience harm, do not suffer, do not fear consequences, do not regret.
So they cannot do moral reasoning.
We are offloading moral reasoning to systems that cannot do moral reasoning.
What can go wrong?
*
Full piece in the first reply
The creator of Linux just publicly called out the AI hype. Word for word.
Linus Torvalds took the stage at Open Source Summit 2026 and said this:
"When I see people saying 99% of our code is written by AI, I literally get angry. Because those same people — I can pretty much guarantee — 100% of their code is written by compilers. But they never say that."
He is not anti AI. The Linux kernel saw a 20% jump in submissions this release because of AI tools. He uses it. He gets it.
His point is something most people are too afraid to say.
AI is a productivity tool exactly like compilers were. Compilers boosted programming by 1000x. AI adds another 10x on top. Enormous. But nobody says "the compiler wrote my code." So why are we saying AI wrote it?
He also flagged something nobody is talking about.
AI is flooding small open source projects with drive-by bug reports. Someone runs a prompt, files a report and disappears when asked for a patch. Maintainers with one or two people are drowning trying to keep up.
"Sometimes AI reports a bug and when you ask for more information the person has done that drive-by and does not even answer your question. That is the real burnout issue."
And his final warning was the sharpest of all.
"People who do not understand the complexity of systems will prompt systems and write processes that will fail."
The AI hype crowd is very loud right now.
Linus has been building real systems for 35 years. When he talks, engineers listen.
Full interview here:
https://t.co/LmXJtvKc4O
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X
@x
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